Download Dataset XYZ file Description: Configurations of Ag from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Ag_AndolinaSaidi__DS_q4h7q8q0fnve_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Ag
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Ag
Number of Configurations: 3,795
Number of Elements: 1
Number of Atoms: 104,827
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Al from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Al_AndolinaSaidi__DS_8y775we7um7w_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Al
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Al
Number of Configurations: 2,572
Number of Elements: 1
Number of Atoms: 88,139
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Au from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Au_AndolinaSaidi__DS_iie3c31ar46x_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Au
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Au
Number of Configurations: 3,601
Number of Elements: 1
Number of Atoms: 89,366
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Co from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Co_AndolinaSaidi__DS_jt0lax9yd15r_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Co
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Co
Number of Configurations: 3,356
Number of Elements: 1
Number of Atoms: 67,320
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Cu_AndolinaSaidi__DS_dc3o40aou2le_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Cu
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Cu
Number of Configurations: 3,366
Number of Elements: 1
Number of Atoms: 96,568
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Ge from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Ge_AndolinaSaidi__DS_90fnsmavv1am_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Ge
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Ge
Number of Configurations: 2,895
Number of Elements: 1
Number of Atoms: 195,270
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of I from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-I_AndolinaSaidi__DS_gc1y80tpyylb_0
Name: 23-Single-Element-DNPs_RSCDD_2023-I
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: I
Number of Configurations: 4,532
Number of Elements: 1
Number of Atoms: 115,902
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Kr from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Kr_AndolinaSaidi__DS_omnl1yy49sdh_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Kr
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Kr
Number of Configurations: 2,975
Number of Elements: 1
Number of Atoms: 97,920
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Li from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Li_AndolinaSaidi__DS_wyo91w20wlgm_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Li
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Li
Number of Configurations: 2,536
Number of Elements: 1
Number of Atoms: 93,724
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Mg from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Mg_AndolinaSaidi__DS_mevqyitwxukc_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Mg
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Mg
Number of Configurations: 3,004
Number of Elements: 1
Number of Atoms: 58,567
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Mo_AndolinaSaidi__DS_bjkftn0ug9r3_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Mo
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Mo
Number of Configurations: 3,718
Number of Elements: 1
Number of Atoms: 66,612
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Nb from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Nb_AndolinaSaidi__DS_zbxayq0diq6l_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Nb
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Nb
Number of Configurations: 3,246
Number of Elements: 1
Number of Atoms: 56,191
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Ni from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Ni_AndolinaSaidi__DS_nue14kckbkdh_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Ni
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Ni
Number of Configurations: 3,817
Number of Elements: 1
Number of Atoms: 75,534
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Os from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Os_AndolinaSaidi__DS_098x6q7kbeat_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Os
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Os
Number of Configurations: 4,779
Number of Elements: 1
Number of Atoms: 117,968
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Pb from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Pb_AndolinaSaidi__DS_k065jfggbq43_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Pb
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Pb
Number of Configurations: 5,350
Number of Elements: 1
Number of Atoms: 119,252
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Pd from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Pd_AndolinaSaidi__DS_g0sb0h7usqw7_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Pd
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Pd
Number of Configurations: 3,478
Number of Elements: 1
Number of Atoms: 140,196
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Pt from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Pt_AndolinaSaidi__DS_0zgz34a90a6i_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Pt
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Pt
Number of Configurations: 2,609
Number of Elements: 1
Number of Atoms: 62,152
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Re_AndolinaSaidi__DS_gzbvdicu231p_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Re
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Re
Number of Configurations: 5,029
Number of Elements: 1
Number of Atoms: 101,248
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Sb from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Sb_AndolinaSaidi__DS_z90lfjg88fzo_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Sb
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Sb
Number of Configurations: 5,529
Number of Elements: 1
Number of Atoms: 122,289
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Sr from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Sr_AndolinaSaidi__DS_o3itca7mk80r_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Sr
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Sr
Number of Configurations: 3,155
Number of Elements: 1
Number of Atoms: 49,426
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Ti from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Ti_AndolinaSaidi__DS_ofpcyxez6xsc_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Ti
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Ti
Number of Configurations: 5,665
Number of Elements: 1
Number of Atoms: 153,659
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Zn from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Zn_AndolinaSaidi__DS_xsad0btc0bsn_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Zn
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Zn
Number of Configurations: 4,052
Number of Elements: 1
Number of Atoms: 107,039
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Configurations of Zr from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

ColabFit ID: 23-Single-Element-DNPs_RSCDD_2023-Zr_AndolinaSaidi__DS_h9w8v7289utq_0
Name: 23-Single-Element-DNPs_RSCDD_2023-Zr
Authors: Christopher M. Andolina, Wissam A. Saidi
Elements: Zr
Number of Configurations: 4,730
Number of Elements: 1
Number of Atoms: 81,165
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/saidigroup/23-Single-Element-DNPs
https://doi.org/10.1039/D3DD00046J

Download Dataset XYZ file Description: Reference C, H, O, and N atoms from 3BPA, used to showcase the performance of linear atomic cluster expansion (ACE) force fields in a machine learning model to predict the potential energy surfaces of organic molecules.

ColabFit ID: 3BPA_isolated_atoms_KovacsOordKuceraAllenColeOrtnerCsanyi__DS_gaq0fec8i6ik_0
Name: 3BPA_isolated_atoms
Authors: Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, Gábor Csányi
Elements: N, H, C, O
Number of Configurations: 4
Number of Elements: 4
Number of Atoms: 4
Property Types: potential-energy

Links:
https://doi.org/10.1021/acs.jctc.1c00647
https://doi.org/10.1021/acs.jctc.1c00647

Download Dataset XYZ file Description: Test configurations with MD simulations performed at 1200K from 3BPA, used to showcase the performance of linear atomic cluster expansion (ACE) force fields in a machine learning model to predict the potential energy surfaces of organic molecules.

ColabFit ID: 3BPA_test_1200K_KovacsOordKuceraAllenColeOrtnerCsanyi__DS_xaasqmrdv28s_0
Name: 3BPA_test_1200K
Authors: Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, Gábor Csányi
Elements: C, H, N, O
Number of Configurations: 2,139
Number of Elements: 4
Number of Atoms: 57,753
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1021/acs.jctc.1c00647
https://doi.org/10.1021/acs.jctc.1c00647

Download Dataset XYZ file Description: Test configurations with MD simulations performed at 300K from 3BPA, used to showcase the performance of linear atomic cluster expansion (ACE) force fields in a machine learning model to predict the potential energy surfaces of organic molecules.

ColabFit ID: 3BPA_test_300K_KovacsOordKuceraAllenColeOrtnerCsanyi__DS_i4hwummq4c7j_0
Name: 3BPA_test_300K
Authors: Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, Gábor Csányi
Elements: C, H, N, O
Number of Configurations: 1,669
Number of Elements: 4
Number of Atoms: 45,063
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1021/acs.jctc.1c00647
https://doi.org/10.1021/acs.jctc.1c00647

Download Dataset XYZ file Description: Test configurations with MD simulations performed at 600K from 3BPA, used to showcase the performance of linear atomic cluster expansion (ACE) force fields in a machine learning model to predict the potential energy surfaces of organic molecules.

ColabFit ID: 3BPA_test_600K_KovacsOordKuceraAllenColeOrtnerCsanyi__DS_epqxcavda3kt_0
Name: 3BPA_test_600K
Authors: Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, Gábor Csányi
Elements: C, H, N, O
Number of Configurations: 2,138
Number of Elements: 4
Number of Atoms: 57,726
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1021/acs.jctc.1c00647
https://doi.org/10.1021/acs.jctc.1c00647

Download Dataset XYZ file Description: Test configurations with fixed value for dihedral beta in alpha-gamma plane of 120 degreesfrom 3BPA dataset. Used to showcase the performance of linear atomic cluster expansion (ACE) force fields in a machine learning model to predict the potential energy surfaces of organic molecules.

ColabFit ID: 3BPA_test_dih_beta120_KovacsOordKuceraAllenColeOrtnerCsanyi__DS_8cg3pdvxt0pa_0
Name: 3BPA_test_dih_beta120
Authors: Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, Gábor Csányi
Elements: C, H, N, O
Number of Configurations: 2,347
Number of Elements: 4
Number of Atoms: 63,369
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1021/acs.jctc.1c00647
https://doi.org/10.1021/acs.jctc.1c00647

Download Dataset XYZ file Description: Test configurations with fixed value for dihedral beta in alpha-gamma plane of 150 degreesfrom 3BPA dataset. Used to showcase the performance of linear atomic cluster expansion (ACE) force fields in a machine learning model to predict the potential energy surfaces of organic molecules.

ColabFit ID: 3BPA_test_dih_beta150_KovacsOordKuceraAllenColeOrtnerCsanyi__DS_199ama4h9t7m_0
Name: 3BPA_test_dih_beta150
Authors: Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, Gábor Csányi
Elements: C, H, N, O
Number of Configurations: 2,350
Number of Elements: 4
Number of Atoms: 63,450
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1021/acs.jctc.1c00647
https://doi.org/10.1021/acs.jctc.1c00647

Download Dataset XYZ file Description: Test configurations with fixed value for dihedral beta in alpha-gamma plane of 180 degreesfrom 3BPA dataset. Used to showcase the performance of linear atomic cluster expansion (ACE) force fields in a machine learning model to predict the potential energy surfaces of organic molecules.

ColabFit ID: 3BPA_test_dih_beta180_KovacsOordKuceraAllenColeOrtnerCsanyi__DS_ilijykl0t0fn_0
Name: 3BPA_test_dih_beta180
Authors: Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, Gábor Csányi
Elements: C, H, N, O
Number of Configurations: 2,350
Number of Elements: 4
Number of Atoms: 63,450
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1021/acs.jctc.1c00647
https://doi.org/10.1021/acs.jctc.1c00647

Download Dataset XYZ file Description: Training configurations with MD simulations performed at 300K from 3BPA, used to showcase the performance of linear atomic cluster expansion (ACE) force fields in a machine learning model to predict the potential energy surfaces of organic molecules.

ColabFit ID: 3BPA_train_300K_KovacsOordKuceraAllenColeOrtnerCsanyi__DS_hu0btdblv8x6_0
Name: 3BPA_train_300K
Authors: Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, Gábor Csányi
Elements: C, H, N, O
Number of Configurations: 500
Number of Elements: 4
Number of Atoms: 13,500
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1021/acs.jctc.1c00647
https://doi.org/10.1021/acs.jctc.1c00647

Download Dataset XYZ file Description: Training configurations with MD simulation performed at 300K, 600K and 1200K from 3BPA dataset, used to showcase the performance of linear atomic cluster expansion (ACE) force fields in a machine learning model to predict the potential energy surfaces of organic molecules.

ColabFit ID: 3BPA_train_mixed_KovacsOordKuceraAllenColeOrtnerCsanyi__DS_v88913tvdxbr_0
Name: 3BPA_train_mixed
Authors: Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, Gábor Csányi
Elements: C, H, N, O
Number of Configurations: 500
Number of Elements: 4
Number of Atoms: 13,500
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1021/acs.jctc.1c00647
https://doi.org/10.1021/acs.jctc.1c00647

Download Dataset XYZ file Description: This dataset was used for the training of an MLIP for amorphous alumina (a-AlOx). Two configurations sets correspond to i) the actual training data and ii) additional reference data. Ab initio calculations were performedwith the Vienna Ab initio Simulation Package. The projector augmented wave method was used to treat the atomic core electrons,and the Perdew-Burke-Ernzerhof functional within the generalized gradient approximation was used to describe the electron-electron interactions. The cutoff energy for the plane-wave basis set was set to 550 eV during the ab initio calculation. The obtained reference database includes the DFT energies of 41,203 structures. The supercell size of the AlOx reference structures varied from 24 to 132 atoms. K-point values are given for structures with: Al0, Al12, Al24, Al48 and Al192.

ColabFit ID: a-AlOx_JCP_2020_LiAndoWatanabe__DS_70btumen3361_0
Name: a-AlOx_JCP_2020
Authors: Wenwen Li, Yasunobu Ando, Satoshi Watanabe
Elements: Al, O
Number of Configurations: 123,586
Number of Elements: 2
Number of Atoms: 4,541,918
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:y1-zd
https://doi.org/10.1063/5.0026289

Download Dataset XYZ file Description: Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth).

ColabFit ID: ABC2D6-16_PRL_2018_FaberLindmaaLilienfeldArmiento__DS_0ady7a8a8n6p_0
Name: ABC2D6-16_PRL_2018
Authors: Felix Faber, Alexander Lindmaa, O. Anatole von Lilienfeld, Rickard Armiento
Elements: As, B, In, N, F, Ga, O, Ge, Si, Sn, Cs, Se, Sb, Al, Ba, H, P, Ar, K, Li, S, C, Mg, Be, Rb, Sr, He, Ca, I, Na, Br, Pb, Cl, Kr, Xe, Ne, Tl, Te, Bi
Number of Configurations: 21,882
Number of Elements: 39
Number of Atoms: 218,820
Property Types: formation-energy

Links:
https://qmml.org/datasets.html
https://doi.org/10.1103/PhysRevLett.117.135502

Download Dataset XYZ file Description: The amorphous carbon dataset was generated using ab initio calculations with VASP software. We utilized the LDA exchange-correlation functional and the PAW potential for carbon. Melt-quench simulations were performed to create amorphous and liquid-state structures. A simple cubic lattice of 216 carbon atoms was chosen as the initial state. Simulations were conducted at densities of 1.5, 1.7, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, and 3.5 g/cm3 to produce a variety of structures. The NVT ensemble was employed for all melt-quench simulations, and the density was adjusted by modifying the size of the simulation cell. A time step of 1 fs was used for the simulations. For all densities, only the Γ points were sampled in the k-space. To increase structural diversity, six independent simulations were performed.In the melt-quench simulations, the temperature was raised from 300 K to 9000 K over 2 ps to melt carbon. Equilibrium molecular dynamics (MD) was conducted at 9000 K for 3 ps to create a liquid state, followed by a decrease in temperature to 5000 K over 2 ps, with the system equilibrating at that temperature for 2 ps. Finally, the temperature was lowered from 5000 K to 300 K over 2 ps to generate an amorphous structure.During the melt-quench simulation, 30 snapshots were taken from the equilibrium MD trajectory at 9000 K, 100 from the cooling process between 9000 and 5000 K, 25 from the equilibrium MD trajectory at 5000 K, and 100 from the cooling process between 5000 and 300 K. This yielded a total of 16,830 data points.Data for diamond structures containing 216 atoms at densities of 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, and 3.5 g/cm3 were also prepared. Further data on the diamond structure were obtained from 80 snapshots taken from the 2 ps equilibrium MD trajectory at 300 K, resulting in 560 data points.To validate predictions for larger structures, we generated data for 512-atom systems using the same procedure as for the 216-atom systems. A single simulation was conducted for each density. The number of data points was 2,805 for amorphous and liquid states

ColabFit ID: aC_JCP_2023_MinamitaniObayashiShimizuWatanabe__DS_bmjfal3bj4ah_0
Name: aC_JCP_2023
Authors: Emi Minamitani, Ippei Obayashi, Koji Shimizu, Satoshi Watanabe
Elements: C
Number of Configurations: 20,195
Number of Elements: 1
Number of Atoms: 5,192,400
Property Types: potential-energy, free-energy, atomic-forces

Links:
https://doi.org/10.5281/zenodo.7905585
https://doi.org/10.1063/5.0159349

Download Dataset XYZ file Description: Test split from the 216-atom amorphous portion of the aC_JCP_2023 dataset. The amorphous carbon dataset was generated using ab initio calculations with VASP software. We utilized the LDA exchange-correlation functional and the PAW potential for carbon. Melt-quench simulations were performed to create amorphous and liquid-state structures. A simple cubic lattice of 216 carbon atoms was chosen as the initial state. Simulations were conducted at densities of 1.5, 1.7, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, and 3.5 g/cm3 to produce a variety of structures. The NVT ensemble was employed for all melt-quench simulations, and the density was adjusted by modifying the size of the simulation cell. A time step of 1 fs was used for the simulations. For all densities, only the Γ points were sampled in the k-space. To increase structural diversity, six independent simulations were performed.In the melt-quench simulations, the temperature was raised from 300 K to 9000 K over 2 ps to melt carbon. Equilibrium molecular dynamics (MD) was conducted at 9000 K for 3 ps to create a liquid state, followed by a decrease in temperature to 5000 K over 2 ps, with the system equilibrating at that temperature for 2 ps. Finally, the temperature was lowered from 5000 K to 300 K over 2 ps to generate an amorphous structure.During the melt-quench simulation, 30 snapshots were taken from the equilibrium MD trajectory at 9000 K, 100 from the cooling process between 9000 and 5000 K, 25 from the equilibrium MD trajectory at 5000 K, and 100 from the cooling process between 5000 and 300 K. This yielded a total of 16,830 data points.Data for diamond structures containing 216 atoms at densities of 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, and 3.5 g/cm3 were also prepared. Further data on the diamond structure were obtained from 80 snapshots taken from the 2 ps equilibrium MD trajectory at 300 K, resulting in 560 data points.To validate predictions for larger structures, we generated data for 512-atom systems using the same procedure as for the 216-atom systems. A single simulation was conducted for each density. The number of data points was 2,805 for amorphous and liquid states

ColabFit ID: aC_JCP_2023_test_MinamitaniObayashiShimizuWatanabe__DS_sany2behzwv8_0
Name: aC_JCP_2023_test
Authors: Emi Minamitani, Ippei Obayashi, Koji Shimizu, Satoshi Watanabe
Elements: C
Number of Configurations: 3,366
Number of Elements: 1
Number of Atoms: 727,056
Property Types: potential-energy, free-energy, atomic-forces

Links:
https://doi.org/10.5281/zenodo.7905585
https://doi.org/10.1063/5.0159349

Download Dataset XYZ file Description: Test split from the 216-atom amorphous portion of the aC_JCP_2023 dataset. The amorphous carbon dataset was generated using ab initio calculations with VASP software. We utilized the LDA exchange-correlation functional and the PAW potential for carbon. Melt-quench simulations were performed to create amorphous and liquid-state structures. A simple cubic lattice of 216 carbon atoms was chosen as the initial state. Simulations were conducted at densities of 1.5, 1.7, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, and 3.5 g/cm3 to produce a variety of structures. The NVT ensemble was employed for all melt-quench simulations, and the density was adjusted by modifying the size of the simulation cell. A time step of 1 fs was used for the simulations. For all densities, only the Γ points were sampled in the k-space. To increase structural diversity, six independent simulations were performed.In the melt-quench simulations, the temperature was raised from 300 K to 9000 K over 2 ps to melt carbon. Equilibrium molecular dynamics (MD) was conducted at 9000 K for 3 ps to create a liquid state, followed by a decrease in temperature to 5000 K over 2 ps, with the system equilibrating at that temperature for 2 ps. Finally, the temperature was lowered from 5000 K to 300 K over 2 ps to generate an amorphous structure.During the melt-quench simulation, 30 snapshots were taken from the equilibrium MD trajectory at 9000 K, 100 from the cooling process between 9000 and 5000 K, 25 from the equilibrium MD trajectory at 5000 K, and 100 from the cooling process between 5000 and 300 K. This yielded a total of 16,830 data points.Data for diamond structures containing 216 atoms at densities of 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, and 3.5 g/cm3 were also prepared. Further data on the diamond structure were obtained from 80 snapshots taken from the 2 ps equilibrium MD trajectory at 300 K, resulting in 560 data points.To validate predictions for larger structures, we generated data for 512-atom systems using the same procedure as for the 216-atom systems. A single simulation was conducted for each density. The number of data points was 2,805 for amorphous and liquid states

ColabFit ID: aC_JCP_2023_train_MinamitaniObayashiShimizuWatanabe__DS_20h8o5d0ltx1_0
Name: aC_JCP_2023_train
Authors: Emi Minamitani, Ippei Obayashi, Koji Shimizu, Satoshi Watanabe
Elements: C
Number of Configurations: 13,464
Number of Elements: 1
Number of Atoms: 2,908,224
Property Types: potential-energy, free-energy, atomic-forces

Links:
https://doi.org/10.5281/zenodo.7905585
https://doi.org/10.1063/5.0159349

Download Dataset XYZ file Description: This dataset consists of graphene superlattices with tungsten adatoms with properties calculated at the DFT level of theory. The authors modeled the placement of tungsten adatoms on a graphene monolayer. The resulting superlattice structures were then used to calculate electronic band structure and phonon dispersion relations. The dataset was used to investigate the effect of adatom placement on electronic band structure and phonon dispersion relations of graphene superlattices. The creation of the dataset involved the following steps: 1. Selection of the graphene monolayer as the starting point for the superlattice construction. 2. Placement of tungsten adatoms in the center of the unit cell 3. Calculation of the electronic structure and other properties of the resulting superlattice using DFT. 4. Generation of a set of reduced Brillouin zones representing the symmetry of the superlattice. 5. Calculation of the electronic band structure and phonon dispersion relations for each superlattice structure in the dataset.

ColabFit ID: adatoms_on_single-layer_graphene_PRR2021_SkurativskaTsirkinNattererNeupertFischer__DS_08feepssbuq5_0
Name: adatoms_on_single-layer_graphene_PRR2021
Authors: Anastasiia Skurativska, Stepan S. Tsirkin, Fabian D Natterer, Titus Neupert, Mark H Fischer
Elements: C, Rh, Ir, Mo, Os, W, Ta, Cr, Nb, Ru, Re
Number of Configurations: 18
Number of Elements: 11
Number of Atoms: 774
Property Types: atomic-forces, potential-energy, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:bj-bh
http://doi.org/10.1103/PhysRevResearch.3.L032003

Download Dataset XYZ file Description: The amorphous LiSi data set comprises 45,169 atomic structures with compositions Li(x)Si (0.0≤x≤4.75) and the corresponding energies and interatomic forces, which were generated using an iterative approach based on an evolutionary algorithm and subsequent refinement, as described in detail in reference [15]. The data includes bulk, surface, and cluster structures with system sizes of up to 608 atoms. The energies and forces of the LiSi structures were obtained from DFT calculations using the Perdew-Burke-Ernzerhof [10] exchange-correlation functional and projector-augmented wave pseudopotentials [16], as implemented in the Vienna Ab-Initio Simulation Package (VASP) [17,18]. We employed a plane-wave basis set with an energy cutoff of 520 eV for the representation of the wavefunctions and a uniform gamma-centered k-point grid for the Brillouin zone integration, with a mesh density corresponding to a number of k points of at least 1000 divided by the number of atoms. The atomic positions and lattice parameters of all structures were optimized until residual forces were below 20 meV/Å. This dataset was also used for the construction of the ANN potential in Ref. [15] and [19]. [10] J. P. Perdew, K. Burke, and M. Ernzerhof, Phys. Rev. Lett. 77, 3865 (1996). [15] N. Artrith, A. Urban, G. Ceder, J. Chem. Phys. 148 (2018) 241711. [16] P. E. Blöchl, Phys. Rev. B 50, 17953–17979 (1994). [17] G. Kresse, J. Furthmüller, Phys. Rev. B 54, 11169–11186 (1996). [18] Kresse, J. Furthmüller, Comput. Mater. Sci. 6, 15–50 (1996). [19] N. Artrith, A. Urban, Y. Wang, G. Ceder, arXiv:1901.09272, https://arxiv.org/pdf/1901.09272.pdf

ColabFit ID: AENET_amorphous_LiSi_JCP2021_ChenMorawietzMarklandArtrith__DS_0nnbymlcjota_0
Name: AENET_amorphous_LiSi_JCP2021
Authors: Michael S. Chen, Tobias Morawietz, Thomas E. Markland, Nongnuch Artrith
Elements: Li, Si
Number of Configurations: 44,652
Number of Elements: 2
Number of Atoms: 5,741,142
Property Types: atomic-forces, potential-energy

Links:
https://doi.org/10.24435/materialscloud:dx-ct
http://doi.org/10.1063/5.0063880

Download Dataset XYZ file Description: The water data set comprises energies and forces of 9,189 condensed-phase structures. The data was obtained in an iterative procedure described in detail in Ref. [4]. The final ANN potential was employed in Refs. [4,5] to analyze temperature-dependent Raman spectra of liquid water. The data set contains structures from four iterations: Initial structures (iteration 0) were obtained from classical and path integral AIMD simulations of bulk liquid water in a cubic box containing 64 water molecules at 300 K as reported in Ref. [6]. Distorted configurations with higher forces were added by randomly displacing the Cartesian coordinates of these configurations. Iteration 1 contains a set of 500 configurations from MD simulations with the fully flexible SPC/E flex water model [7] employing a 25 % increased water density (simulation box with 80 water molecules) and elevated temperatures (T = 500 K) in order to sample highly repulsive configurations. Structures in iteration 2 were obtained by classical MD simulations with preliminary ANN potentials at T = 300 K, 325 K, 350 K, and 370 K employing cubic boxes with 64 molecules and the corresponding experimental densities. The final iteration 3 data contains structures from preliminary ANN simulations with classical and quantum nuclei, respectively, at a wide range of temperatures (T = 258 K, 268 K, 280 K, 290 K, 300 K, 310 K, 320 K, 330 K, 340 K, 350 K, 360 K, and 370 K) using cubic boxes with 64 molecules and the corresponding experimental densities. Energies and atomic forces were calculated with the CP2K program [8,9] using the revPBE exchange-correlation functional [10,11] with D3 dispersion correction [12] following the setup reported in Ref. [4]. Atomic cores were represented using the dual-space Goedecker-Teter-Hutter pseudopotentials [13], Kohn-Sham orbitals were expanded in the TZV2P basis set within the GPW method [14], and the density was represented by an auxiliary plane-wave basis with a cutoff of 400 Ry. [1] A. Kokalj, J. Mol. Graphics Modell. 17, 176–179 (1999). [2] N. Artrith, A. Urban, Comput. Mater. Sci. 114, 135–150 (2016). [3] N. Artrith, A. Urban, G. Ceder, Phys. Rev. B 96, 014112 (2017). [4] T. Morawietz, O. Marsalek, S. R. Pattenaude, L. M. Streacker, D. Ben-Amotz, and T. E. Markland, J. Phys. Chem. Lett. 9, 851 (2018). [5] T. Morawietz, A. S. Urbina, P. K. Wise, X. Wu, W. Lu, D. Ben-Amotz, and T. E. Markland, J. Phys. Chem. Lett. 10, 6067 (2019). [6] Marsalek and T. E. Markland, J. Phys. Chem. Lett. 8, 1545 (2017). [7] X. B. Zhang, Q. L. Liu, and A. M. Zhu, Fluid Ph. Equilibria 262, 210(2007). [8] J. VandeVondele, M. Krack, F. Mohamed, M. Parrinello, T. Chassaing, and J. Hutter, Comput. Phys. Commun. 167, 103 (2005). [9] J. Hutter, M. Iannuzzi, F. Schiffmann, and J. VandeVondele, WIRES Comput. Mol. Sci. 4, 15 (2014). [10] J. P. Perdew, K. Burke, and M. Ernzerhof, Phys. Rev. Lett. 77, 3865 (1996). [11] Y. Zhang and W. Yang, Phys. Rev. Lett. 80, 890 (1998). [12] S. Grimme, J. Antony, S. Ehrlich, and H. Krieg, J. Chem. Phys. 132, 154104 (2010). [13] S. Goedecker, M. Teter, and J. Hutter, Phys. Rev. B 54, 1703 (1996). [14] B. G. Lippert, J. Hutter, and M. Parrinello, Mol. Phys. 92, 477 (1997).

ColabFit ID: AENET_liquid_water_dataset_JCP2021_ChenMorawietzMarklandArtrith__DS_v4bk24pq0had_0
Name: AENET_liquid_water_dataset_JCP2021
Authors: Michael S. Chen, Tobias Morawietz, Thomas E. Markland, Nongnuch Artrith
Elements: O, H
Number of Configurations: 9,189
Number of Elements: 2
Number of Atoms: 1,788,288
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:dx-ct
http://doi.org/10.1063/5.0063880

Download Dataset XYZ file Description: Approximately 145,000 configurations of alkane, aspirin, alpha-glucose and uracil, partly taken from the MD-17 dataset, used in training an 'Atomic Neural Net' model.

ColabFit ID: AFF_JCP_2022_LiZhouSebastianWuGu__DS_5lhmgnxhuia3_0
Name: AFF_JCP_2022
Authors: Hao Li, Musen Zhou, Jessalyn Sebastian, Jianzhong Wu, Mengyang Gu
Elements: C, H, N, O
Number of Configurations: 143,770
Number of Elements: 4
Number of Atoms: 1,911,240
Property Types: potential-energy, atomic-forces

Links:
https://github.com/UncertaintyQuantification/AFF/tree/master
https://doi.org/10.1063/5.0088017

Download Dataset XYZ file Description: Approximately 7,600 configurations of Ag used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.

ColabFit ID: Ag-PBE_MSMSE_2021_WangWangZhangXuWang__DS_6e5ljaqa3cfr_0
Name: Ag-PBE_MSMSE_2021
Authors: Yinan Wang, Xiaoyang Wang, Linfeng Zhang, Ben Xu, Han Wang
Elements: Ag
Number of Configurations: 7,608
Number of Elements: 1
Number of Atoms: 152,318
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://www.aissquare.com/datasets/detail?pageType=datasets&name=Ag-PBE
https://doi.org/10.48550/arXiv.2108.06232

Download Dataset XYZ file Description: Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.

ColabFit ID: AgAu-nanoalloy_MSMSE_2021_WangWangZhangXuWang__DS_t0ec1irmeo59_0
Name: AgAu-nanoalloy_MSMSE_2021
Authors: Yinan Wang, Xiaoyang Wang, Linfeng Zhang, Ben Xu, Han Wang
Elements: Ag, Au
Number of Configurations: 51,771
Number of Elements: 2
Number of Atoms: 1,188,220
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy
https://doi.org/10.48550/arXiv.2108.06232

Download Dataset XYZ file Description: The dataset consists of energies, forces and virials for DFT-VASP-generated Ag-Pd systems. The data was used to fit an active learned dataset which was used to compare MTP- and SOAP-GAP-generated potentials.

ColabFit ID: AgPd_NPJ_2021_RosenbrockGubaevShapeevPartayBernsteinCsanyiHart__DS_y5m3hunroa7x_0
Name: AgPd_NPJ_2021
Authors: Conrad W. Rosenbrock, Konstantin Gubaev, Alexander V. Shapeev, Livia B. Pártay, Noam Bernstein, Gábor Csányi, Gus L. W. Hart
Elements: Ag, Pd
Number of Configurations: 1,691
Number of Elements: 2
Number of Atoms: 14,180
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/msg-byu/agpd
https://doi.org/10.1038/s41524-020-00477-2

Download Dataset XYZ file Description: Dataset from "Stress-dependence of generalized stacking fault energies":DFT calculations of generalized stacking fault energies (GSFE) for Al, Cu, and Mg.

ColabFit ID: Al_Cu_Mg_GSFE_JMPS2019_YinAndricCurtin__DS_styimm9cal6v_0
Name: Al_Cu_Mg_GSFE_JMPS2019
Authors: Binglun Yin, Predrag Andric, W. A. Curtin
Elements: Cu, Mg, Al
Number of Configurations: 273
Number of Elements: 3
Number of Atoms: 3,264
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:2019.0089/v1
https://doi.org/10.1016/j.jmps.2018.09.007

Download Dataset XYZ file Description: This dataset is formed from two parts: single-species datasets for Al, Ni, and Cu from the NOMAD Encyclopedia and multi-species datasets that include Al, Ni and Cu from NOMAD Archive. Duplicates have been removed from NOMAD Encyclopedia data. For the multi-species data, only the last configuration steps for each NOMAD Archive record were used because the last configuration typically cooresponds with a fully relaxed configuration. In this dataset, the NOMAD unique reference access IDs are retained along with a subset of their meta information that includes whether the supplied configuration is from a converged calculation as well as the Density Functional Theory (DFT) code, version, and type of DFT functionals with the total potential energies. This dataset consists of 39.1% Al, 30.7% Ni, and 30.2% Cu and has 27,987 atomic environments in 3337 structures.

ColabFit ID: AlNiCu_AIP_2020_OnatOrtnerKermode__DS_vmnudsz7kx0a_0
Name: AlNiCu_AIP_2020
Authors: Berk Onat, Christoph Ortner, James R. Kermode
Elements: Ni, Al, Cu
Number of Configurations: 1,017
Number of Elements: 3
Number of Atoms: 4,650
Property Types: potential-energy, free-energy

Links:
https://github.com/DescriptorZoo/sensitivity-dimensionality-results
https://doi.org/10.1063/5.0016005

Download Dataset XYZ file Description: This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms.

ColabFit ID: AlNiTi_CMS_2019_GubaevPodryabinkinHartShapeev__DS_dtjyh96dypuu_0
Name: AlNiTi_CMS_2019
Authors: Konstantin Gubaev, Evgeny V. Podryabinkin, Gus L.W. Hart, Alexander V. Shapeev
Elements: Al, Ni, Ti
Number of Configurations: 2,684
Number of Elements: 3
Number of Atoms: 25,067
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data
https://doi.org/10.1016/j.commatsci.2018.09.031

Download Dataset XYZ file Description: 53,841 structures of alpha-brass (less than 40% Zinc). Includes atomic forces and total energy. Calculated using VASP at the DFT level of theory.

ColabFit ID: alpha_brass_nanoparticles_WeinreichRomerPaleicoBehler__DS_agiti2oe5bqb_0
Name: alpha_brass_nanoparticles
Authors: Jan Weinreich, Anton Römer, Martín Leandro Paleico, Jörg Behler
Elements: Cu, Zn
Number of Configurations: 53,696
Number of Elements: 2
Number of Atoms: 2,956,679
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:94-aq
https://doi.org/10.1021/acs.jpcc.1c02314
http://doi.org/10.1021/acs.jpcc.0c00559

Download Dataset XYZ file Description: ANI-1 is a dataset of 20 million conformations with calculated non-equilibrium energy values. The conformations are based on a subset of the GDB-11 dataset, each molecule containing between 1 and 8 heavy atoms, with atomic species limited to C, N and O. Configuration sets are included for standard and high energy (defined as energies greater than 275 kcal*mol-1 higher than the lowest energy conformer) conformations, and, within these, number of heavy atoms per molecule.

ColabFit ID: ANI-1_SmithIsayevRoitberg__DS_p4evspy1ntcs_0
Name: ANI-1
Authors: Justin S. Smith, Olexandr Isayev, Adrian E. Roitberg
Elements: H, N, O, C
Number of Configurations: 24,416,306
Number of Elements: 4
Number of Atoms: 392,606,016
Property Types: potential-energy

Links:
https://doi.org/10.6084/m9.figshare.c.3846712.v1
https://doi.org/10.1038/sdata.2017.193

Download Dataset XYZ file Description: ANI-1x contains DFT calculations for approximately 5 million molecular conformations. From an initial training set, an active learning method was used to iteratively add conformations where insufficient diversity was detected. Additional conformations were sampled from existing databases of molecules, such as GDB-11 and ChEMBL. On each of these configurations, one of molecular dynamics sampling, normal mode sampling, dimer sampling, or torsion sampling was performed.

ColabFit ID: ANI-1x_SmithZubatyukNebgenLubbersBarrosRoitbergIsayevTretiak__DS_ko3rpzre7bea_0
Name: ANI-1x
Authors: Justin S. Smith, Roman Zubatyuk, Benjamin Nebgen, Nicholas Lubbers, Kipton Barros, Adrian E. Roitberg, Olexandr Isayev, Sergei Tretiak
Elements: C, H, N, O
Number of Configurations: 4,956,005
Number of Elements: 4
Number of Atoms: 75,700,481
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.6084/m9.figshare.c.4712477.v1
https://doi.org/10.1038/s41597-020-0473-z
https://doi.org/10.1063/1.5023802
https://doi.org/10.1038/s41467-019-10827-4
https://doi.org/10.1126/sciadv.aav6490
https://github.com/aiqm/ANI1x_datasets

Download Dataset XYZ file Description: ANI-2x-B973c-def2mTZVP is a portion of the ANI-2x dataset, which includes DFT-calculated energies for structures from 2 to 63 atoms in size containing H, C, N, O, S, F, and Cl. This portion of ANI-2x was calculated in ORCA at the B973c level of theory using the def2m-TZVP basis set. Configuration sets are divided by number of atoms per structure. Force corrections and dipoles are recorded in the metadata.

ColabFit ID: ANI-2x-B973c-def2mTZVP_HuddlestonZubatyukSmithRoitbergIsayevPickeringDevereuxBarros__DS_gjr8gi1tb8wh_0
Name: ANI-2x-B973c-def2mTZVP
Authors: Kate Huddleston, Roman Zubatyuk, Justin Smith, Adrian Roitberg, Olexandr Isayev, Ignacio Pickering, Christian Devereux, Kipton Barros
Elements: O, F, H, S, Cl, C, N
Number of Configurations: 9,643,594
Number of Elements: 7
Number of Atoms: 146,656,635
Property Types: atomic-forces, potential-energy

Links:
https://doi.org/10.5281/zenodo.10108942
https://doi.org/10.1021/acs.jctc.0c00121

Download Dataset XYZ file Description: ANI-2x-wB97MD3BJ-def2TZVPP is a portion of the ANI-2x dataset, which includes DFT-calculated energies for structures from 2 to 63 atoms in size containing H, C, N, O, S, F, and Cl. This portion of ANI-2x was calculated in ORCA at the wB97M level of theory with D3 and BJ energy corrections, using the def2-TZVPP basis set. Configuration sets are divided by number of atoms per structure. Uncorrected SCF energy values and dipoles are recorded in the metadata.

ColabFit ID: ANI-2x-wB97MD3BJ-def2TZVPP_HuddlestonZubatyukSmithRoitbergIsayevPickeringDevereuxBarros__DS_yxeu6us2i6dh_0
Name: ANI-2x-wB97MD3BJ-def2TZVPP
Authors: Kate Huddleston, Roman Zubatyuk, Justin Smith, Adrian Roitberg, Olexandr Isayev, Ignacio Pickering, Christian Devereux, Kipton Barros
Elements: Cl, H, F, S, O, C, N
Number of Configurations: 9,650,572
Number of Elements: 7
Number of Atoms: 146,715,621
Property Types: potential-energy

Links:
https://doi.org/10.5281/zenodo.10108942
https://doi.org/10.1021/acs.jctc.0c00121

Download Dataset XYZ file Description: ANI-2x-wB97MV-def2TZVPP is a portion of the ANI-2x dataset, which includes DFT-calculated energies for structures from 2 to 63 atoms in size containing H, C, N, O, S, F, and Cl. This portion of ANI-2x was calculated at the WB97MV level of theory using the def2TZVPP basis set. Configuration sets are divided by number of atoms per structure.

ColabFit ID: ANI-2x-wB97MV-def2TZVPP_HuddlestonZubatyukSmithRoitbergIsayevPickeringDevereuxBarros__DS_wxf8f3t3abul_0
Name: ANI-2x-wB97MV-def2TZVPP
Authors: Kate Huddleston, Roman Zubatyuk, Justin Smith, Adrian Roitberg, Olexandr Isayev, Ignacio Pickering, Christian Devereux, Kipton Barros
Elements: O, N, Cl, C, F, H, S
Number of Configurations: 9,650,572
Number of Elements: 7
Number of Atoms: 146,715,621
Property Types: potential-energy

Links:
https://doi.org/10.5281/zenodo.10108942
https://doi.org/10.1021/acs.jctc.0c00121

Download Dataset XYZ file Description: ANI-2x-wB97X-631Gd is a portion of the ANI-2x dataset, which includes DFT-calculated energies for structures from 2 to 63 atoms in size containing H, C, N, O, S, F, and Cl. This portion of ANI-2x was calculated in Gaussian 09 at the wB97X level of theory using the 6-31G(d) basis set. Configuration sets are divided by number of atoms per structure.

ColabFit ID: ANI-2x-wB97X-631Gd_HuddlestonZubatyukSmithRoitbergIsayevPickeringDevereuxBarros__DS_rde4chkdmm18_0
Name: ANI-2x-wB97X-631Gd
Authors: Kate Huddleston, Roman Zubatyuk, Justin Smith, Adrian Roitberg, Olexandr Isayev, Ignacio Pickering, Christian Devereux, Kipton Barros
Elements: F, C, H, Cl, S, N, O
Number of Configurations: 9,651,712
Number of Elements: 7
Number of Atoms: 146,736,809
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.5281/zenodo.10108942
https://doi.org/10.1021/acs.jctc.0c00121

Download Dataset XYZ file Description: Approximately 2800 configurations from a test dataset–one of a pair of train/test datasets of aluminum in crystal and melt phases, used for training and testing an ANI neural network model.

ColabFit ID: ANI-Al_NC2021-test_SmithNebgenMathewChenLubbersBurakovskyTretiakNamGermannFensinBarros__DS_bnnyjpaqb09i_0
Name: ANI-Al_NC2021-test
Authors: Justin S. Smith, Benjamin Nebgen, Nithin Mathew, Jie Chen, Nicholas Lubbers, Leonid Burakovsky, Sergei Tretiak, Hai Ah Nam, Timothy Germann, Saryu Fensin, Kipton Barros
Elements: Al
Number of Configurations: 2,872
Number of Elements: 1
Number of Atoms: 371,645
Property Types: potential-energy, atomic-forces

Links:
https://github.com/atomistic-ml/ani-al
https://doi.org/10.1038/s41467-021-21376-0

Download Dataset XYZ file Description: Approximately 2800 configurations from a train dataset–one of a pair of train/test datasets of aluminum in crystal and melt phases, used for training and testing an ANI neural network model.

ColabFit ID: ANI-Al_NC2021-train_SmithNebgenMathewChenLubbersBurakovskyTretiakNamGermannFensinBarros__DS_tz8gsj3xi2g4_0
Name: ANI-Al_NC2021-train
Authors: Justin S. Smith, Benjamin Nebgen, Nithin Mathew, Jie Chen, Nicholas Lubbers, Leonid Burakovsky, Sergei Tretiak, Hai Ah Nam, Timothy Germann, Saryu Fensin, Kipton Barros
Elements: Al
Number of Configurations: 2,864
Number of Elements: 1
Number of Atoms: 375,121
Property Types: potential-energy, atomic-forces

Links:
https://github.com/atomistic-ml/ani-al
https://doi.org/10.1038/s41467-021-21376-0

Download Dataset XYZ file Description: Approximately 20,000 configurations of Au used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.

ColabFit ID: Au-PBE_MSMSE_2021_WangWangZhangXuWang__DS_tydu7u0h0hhz_0
Name: Au-PBE_MSMSE_2021
Authors: Yinan Wang, Xiaoyang Wang, Linfeng Zhang, Ben Xu, Han Wang
Elements: Au
Number of Configurations: 19,434
Number of Elements: 1
Number of Atoms: 310,792
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://www.aissquare.com/datasets/detail?pageType=datasets&name=Au-PBE
https://doi.org/10.48550/arXiv.2108.06232

Download Dataset XYZ file Description: Dataset (DFT-10B) contains structures of the 10 binary alloys AgCu, AlFe, AlMg, AlNi, AlTi, CoNi, CuFe, CuNi, FeV, and NbNi. Each alloy system includes all possible unit cells with 1-8 atoms for face-centered cubic (fcc) and body-centered cubic (bcc) crystal types, and all possible unit cells with 2-8 atoms for the hexagonal close-packed (hcp) crystal type. This results in 631 fcc, 631 bcc, and 333 hcp structures, yielding 1595 x 10 = 15,950 unrelaxed structures in total. Lattice parameters for each crystal structure were set according to Vegard's law. Total energies were computed using DFT with projector-augmented wave (PAW) potentials within the generalized gradient approximation (GGA) of Perdew, Burke, and Ernzerhof (PBE) as implemented in the Vienna Ab Initio Simulation Package (VASP). The k-point meshes for sampling the Brillouin zone were constructed using generalized regular grids.

ColabFit ID: BA10-18_NyshadhamRuppBekkerShapeevMuellerRosenbrockCsanyiWingateHart__DS_lifzo8zpa76m_0
Name: BA10-18
Authors: Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker, Alexander V. Shapeev, Tim Mueller, Conrad W. Rosenbrock, Gábor Csányi, David W. Wingate, Gus L. W. Hart
Elements: Al, Mg, Fe, V, Cu, Ni, Nb, Ti, Ag, Co
Number of Configurations: 15,920
Number of Elements: 10
Number of Atoms: 116,380
Property Types: potential-energy, atomization-energy

Links:
https://qmml.org/datasets.html
https://doi.org/10.1038/s41524-019-0189-9

Download Dataset XYZ file Description: Dihedral scan about one of the C-C bonds of the conjugated system. Acetylacetone dataset generated from a long molecular dynamics simulation at 300 K using a Langevin thermostat at the semi-empirical GFN2-xTB level of theory. Configurations were sampled at an interval of 1 ps and the resulting set of configurations were recomputed with density functional theory using the PBE exchange-correlation functional with D3 dispersion correction and def2-SVP basis set and VeryTightSCF convergence settings using the ORCA electronic structure package.

ColabFit ID: BOTnet_ACAC_2022_Dihedral_scan_BatatiaBatznerKovacsMusaelianSimmDrautzOrtnerKozinskyCsanyi__DS_0xfqsnoawhoo_0
Name: BOTnet_ACAC_2022_Dihedral_scan
Authors: Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi
Elements: C, H, O
Number of Configurations: 45
Number of Elements: 3
Number of Atoms: 675
Property Types: potential-energy

Links:
https://github.com/davkovacs/BOTNet-datasets
https://doi.org/10.48550/arXiv.2205.06643

Download Dataset XYZ file Description: NEB path of proton transfer reaction between the two forms of acetylacetone. Acetylacetone dataset generated from a long molecular dynamics simulation at 300 K using a Langevin thermostat at the semi-empirical GFN2-xTB level of theory. Configurations were sampled at an interval of 1 ps and the resulting set of configurations were recomputed with density functional theory using the PBE exchange-correlation functional with D3 dispersion correction and def2-SVP basis set and VeryTightSCF convergence settings using the ORCA electronic structure package.

ColabFit ID: BOTnet_ACAC_2022_H_transfer_BatatiaBatznerKovacsMusaelianSimmDrautzOrtnerKozinskyCsanyi__DS_ztlvjmuwskom_0
Name: BOTnet_ACAC_2022_H_transfer
Authors: Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi
Elements: C, H, O
Number of Configurations: 15
Number of Elements: 3
Number of Atoms: 225
Property Types: potential-energy

Links:
https://github.com/davkovacs/BOTNet-datasets
https://doi.org/10.48550/arXiv.2205.06643

Download Dataset XYZ file Description: Energies of the isolated atoms evalauted at the reference DFT settings. Acetylacetone dataset generated from a long molecular dynamics simulation at 300 K using a Langevin thermostat at the semi-empirical GFN2-xTB level of theory. Configurations were sampled at an interval of 1 ps and the resulting set of configurations were recomputed with density functional theory using the PBE exchange-correlation functional with D3 dispersion correction and def2-SVP basis set and VeryTightSCF convergence settings using the ORCA electronic structure package.

ColabFit ID: BOTnet_ACAC_2022_isolated_BatatiaBatznerKovacsMusaelianSimmDrautzOrtnerKozinskyCsanyi__DS_e22u3j5sbilx_0
Name: BOTnet_ACAC_2022_isolated
Authors: Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi
Elements: C, O, H
Number of Configurations: 3
Number of Elements: 3
Number of Atoms: 3
Property Types: potential-energy

Links:
https://github.com/davkovacs/BOTNet-datasets
https://doi.org/10.48550/arXiv.2205.06643

Download Dataset XYZ file Description: Test set of decorrelated geometries sampled from 300 K xTB MD. Acetylacetone dataset generated from a long molecular dynamics simulation at 300 K using a Langevin thermostat at the semi-empirical GFN2-xTB level of theory. Configurations were sampled at an interval of 1 ps and the resulting set of configurations were recomputed with density functional theory using the PBE exchange-correlation functional with D3 dispersion correction and def2-SVP basis set and VeryTightSCF convergence settings using the ORCA electronic structure package.

ColabFit ID: BOTnet_ACAC_2022_test_300K_MD_BatatiaBatznerKovacsMusaelianSimmDrautzOrtnerKozinskyCsanyi__DS_gt0oa9fngod7_0
Name: BOTnet_ACAC_2022_test_300K_MD
Authors: Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi
Elements: C, H, O
Number of Configurations: 650
Number of Elements: 3
Number of Atoms: 9,750
Property Types: potential-energy, atomic-forces

Links:
https://github.com/davkovacs/BOTNet-datasets
https://doi.org/10.48550/arXiv.2205.06643

Download Dataset XYZ file Description: Test set of decorrelated geometries sampled from 600 K xTB MD. Acetylacetone dataset generated from a long molecular dynamics simulation at 300 K using a Langevin thermostat at the semi-empirical GFN2-xTB level of theory. Configurations were sampled at an interval of 1 ps and the resulting set of configurations were recomputed with density functional theory using the PBE exchange-correlation functional with D3 dispersion correction and def2-SVP basis set and VeryTightSCF convergence settings using the ORCA electronic structure package.

ColabFit ID: BOTnet_ACAC_2022_test_600K_MD_BatatiaBatznerKovacsMusaelianSimmDrautzOrtnerKozinskyCsanyi__DS_k0rexhnv58vn_0
Name: BOTnet_ACAC_2022_test_600K_MD
Authors: Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi
Elements: C, H, O
Number of Configurations: 650
Number of Elements: 3
Number of Atoms: 9,750
Property Types: potential-energy, atomic-forces

Links:
https://github.com/davkovacs/BOTNet-datasets
https://doi.org/10.48550/arXiv.2205.06643

Download Dataset XYZ file Description: 500 decorrelated geometries sampled from 300 K xTB MD run. Acetylacetone dataset generated from a long molecular dynamics simulation at 300 K using a Langevin thermostat at the semi-empirical GFN2-xTB level of theory. Configurations were sampled at an interval of 1 ps and the resulting set of configurations were recomputed with density functional theory using the PBE exchange-correlation functional with D3 dispersion correction and def2-SVP basis set and VeryTightSCF convergence settings using the ORCA electronic structure package.

ColabFit ID: BOTnet_ACAC_2022_train_300K_MD_BatatiaBatznerKovacsMusaelianSimmDrautzOrtnerKozinskyCsanyi__DS_8gqwz5bw1pvq_0
Name: BOTnet_ACAC_2022_train_300K_MD
Authors: Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi
Elements: C, H, O
Number of Configurations: 500
Number of Elements: 3
Number of Atoms: 7,500
Property Types: potential-energy, atomic-forces

Links:
https://github.com/davkovacs/BOTNet-datasets
https://doi.org/10.48550/arXiv.2205.06643

Download Dataset XYZ file Description: 500 decorrelated geometries sampled from 600 K xTB MD run. Acetylacetone dataset generated from a long molecular dynamics simulation at 300 K using a Langevin thermostat at the semi-empirical GFN2-xTB level of theory. Configurations were sampled at an interval of 1 ps and the resulting set of configurations were recomputed with density functional theory using the PBE exchange-correlation functional with D3 dispersion correction and def2-SVP basis set and VeryTightSCF convergence settings using the ORCA electronic structure package.

ColabFit ID: BOTnet_ACAC_2022_train_600K_MD_BatatiaBatznerKovacsMusaelianSimmDrautzOrtnerKozinskyCsanyi__DS_632yputkfhpg_0
Name: BOTnet_ACAC_2022_train_600K_MD
Authors: Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi
Elements: C, H, O
Number of Configurations: 500
Number of Elements: 3
Number of Atoms: 7,500
Property Types: potential-energy, atomic-forces

Links:
https://github.com/davkovacs/BOTNet-datasets
https://doi.org/10.48550/arXiv.2205.06643

Download Dataset XYZ file Description: Approximately 115,000 configurations of carbon with 200 atoms, with simulated melt, quench, reheat, then annealing at the noted temperature. Includes a variety of carbon structures.

ColabFit ID: C_Gardner_2022_GardnerBeaulieuDeringer__DS_mma0w04wa4cs_0
Name: C_Gardner_2022
Authors: John L. A. Gardner, Zoé Faure Beaulieu, Volker L. Deringer
Elements: C
Number of Configurations: 115,206
Number of Elements: 1
Number of Atoms: 23,041,200
Property Types: atomic-forces

Links:
https://github.com/jla-gardner/carbon-data
https://doi.org/10.48550/arXiv.2211.16443

Download Dataset XYZ file Description: The dataset consists of energies and forces for monolayer graphene, bilayer graphene, graphite, and diamond in various states, including strained static structures and configurations drawn from ab initio MD trajectories. A total number of 4788 configurations was generated from DFT calculations using the Vienna Ab initio Simulation Package (VASP). The energies and forces are stored in the extended XYZ format. One file for each configuration.

ColabFit ID: C_NPJ2020_WenTadmor__DS_qph0akhjv9kv_0
Name: C_NPJ2020
Authors: Mingjian Wen, Ellad B. Tadmor
Elements: C
Number of Configurations: 4,776
Number of Elements: 1
Number of Atoms: 228,852
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.6084/m9.figshare.12649811.v1
https://doi.org/10.1038/s41524-020-00390-8

Download Dataset XYZ file Description: 6095 isomers of C7O2H10. Energetics were calculated at the G4MP2 level of theory.

ColabFit ID: C7H10O2_RamakrishnanDralRuppLilienfeld__DS_olmzgsz9hmxy_0
Name: C7H10O2
Authors: Raghunathan Ramakrishnan, Pavlo Dral, Matthias Rupp, O. Anatole von Lilienfeld
Elements: C, H, O
Number of Configurations: 6,095
Number of Elements: 3
Number of Atoms: 115,805
Property Types: free-energy, potential-energy

Links:
https://doi.org/10.6084/m9.figshare.c.978904.v5
https://doi.org/10.1038/sdata.2014.22

Download Dataset XYZ file Description: Binning-binning configurations from CA-9 dataset used for training NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials

ColabFit ID: CA-9_BB_training_HedmanRotheJohanssonSandinLarssonMiyamoto__DS_l7inbtql4ea9_0
Name: CA-9_BB_training
Authors: Daniel Hedman, Tom Rothe, Gustav Johansson, Fredrik Sandin, J. Andreas Larsson, Yoshiyuki Miyamoto
Elements: C
Number of Configurations: 20,012
Number of Elements: 1
Number of Atoms: 1,054,055
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:6h-yj
https://doi.org/10.1016/j.cartre.2021.100027

Download Dataset XYZ file Description: Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials

ColabFit ID: CA-9_BB_validation_HedmanRotheJohanssonSandinLarssonMiyamoto__DS_8zft35i5sbgd_0
Name: CA-9_BB_validation
Authors: Daniel Hedman, Tom Rothe, Gustav Johansson, Fredrik Sandin, J. Andreas Larsson, Yoshiyuki Miyamoto
Elements: C
Number of Configurations: 4,003
Number of Elements: 1
Number of Atoms: 233,034
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:6h-yj
https://doi.org/10.1016/j.cartre.2021.100027

Download Dataset XYZ file Description: Binning-random configurations from CA-9 dataset used for training NNP_BR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials

ColabFit ID: CA-9_BR_training_HedmanRotheJohanssonSandinLarssonMiyamoto__DS_fw7m5d8b0fa9_0
Name: CA-9_BR_training
Authors: Daniel Hedman, Tom Rothe, Gustav Johansson, Fredrik Sandin, J. Andreas Larsson, Yoshiyuki Miyamoto
Elements: C
Number of Configurations: 20,013
Number of Elements: 1
Number of Atoms: 1,072,779
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:6h-yj
https://doi.org/10.1016/j.cartre.2021.100027

Download Dataset XYZ file Description: Binning-random configurations from CA-9 dataset used during validation step for NNP_BR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials

ColabFit ID: CA-9_BR_validation_HedmanRotheJohanssonSandinLarssonMiyamoto__DS_fgakfcwhn9zc_0
Name: CA-9_BR_validation
Authors: Daniel Hedman, Tom Rothe, Gustav Johansson, Fredrik Sandin, J. Andreas Larsson, Yoshiyuki Miyamoto
Elements: C
Number of Configurations: 4,002
Number of Elements: 1
Number of Atoms: 214,310
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:6h-yj
https://doi.org/10.1016/j.cartre.2021.100027

Download Dataset XYZ file Description: Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials

ColabFit ID: CA-9_RR_training_HedmanRotheJohanssonSandinLarssonMiyamoto__DS_tag5zubl21w8_0
Name: CA-9_RR_training
Authors: Daniel Hedman, Tom Rothe, Gustav Johansson, Fredrik Sandin, J. Andreas Larsson, Yoshiyuki Miyamoto
Elements: C
Number of Configurations: 20,013
Number of Elements: 1
Number of Atoms: 1,100,042
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:6h-yj
https://doi.org/10.1016/j.cartre.2021.100027

Download Dataset XYZ file Description: Random-random configurations from CA-9 dataset used during validation step for NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials

ColabFit ID: CA-9_RR_validation_HedmanRotheJohanssonSandinLarssonMiyamoto__DS_mr2qaiqh2sx2_0
Name: CA-9_RR_validation
Authors: Daniel Hedman, Tom Rothe, Gustav Johansson, Fredrik Sandin, J. Andreas Larsson, Yoshiyuki Miyamoto
Elements: C
Number of Configurations: 4,002
Number of Elements: 1
Number of Atoms: 218,184
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:6h-yj
https://doi.org/10.1016/j.cartre.2021.100027

Download Dataset XYZ file Description: Test configurations from CA-9 dataset used to evaluate trained NNPs.CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials

ColabFit ID: CA-9_test_HedmanRotheJohanssonSandinLarssonMiyamoto__DS_h0mshvvbxlai_0
Name: CA-9_test
Authors: Daniel Hedman, Tom Rothe, Gustav Johansson, Fredrik Sandin, J. Andreas Larsson, Yoshiyuki Miyamoto
Elements: C
Number of Configurations: 2,727
Number of Elements: 1
Number of Atoms: 206,302
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:6h-yj
https://doi.org/10.1016/j.cartre.2021.100027

Download Dataset XYZ file Description: Configurations from CA-9 dataset used for training NNP_CA-9 potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials

ColabFit ID: CA-9_training_HedmanRotheJohanssonSandinLarssonMiyamoto__DS_9cdtww9pcjqd_0
Name: CA-9_training
Authors: Daniel Hedman, Tom Rothe, Gustav Johansson, Fredrik Sandin, J. Andreas Larsson, Yoshiyuki Miyamoto
Elements: C
Number of Configurations: 40,000
Number of Elements: 1
Number of Atoms: 2,195,399
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:6h-yj
https://doi.org/10.1016/j.cartre.2021.100027

Download Dataset XYZ file Description: Configurations from CA-9 dataset used during validation step for NNP_CA-9 potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials

ColabFit ID: CA-9_validation_HedmanRotheJohanssonSandinLarssonMiyamoto__DS_07zjrtacjg3z_0
Name: CA-9_validation
Authors: Daniel Hedman, Tom Rothe, Gustav Johansson, Fredrik Sandin, J. Andreas Larsson, Yoshiyuki Miyamoto
Elements: C
Number of Configurations: 8,000
Number of Elements: 1
Number of Atoms: 436,601
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:6h-yj
https://doi.org/10.1016/j.cartre.2021.100027

Download Dataset XYZ file Description: Dataset for "Appraisal of calcium ferrites as cathodes for calcium rechargeable batteries: DFT, synthesis, characterization and electrochemistry of Ca4Fe9O17" created to explore Fe-based cathode materials for Ca-ion batteries. Structures include CaFe(2+n)O(4+n), where 0 < n < 3.

ColabFit ID: calcium_ferrites_as_cathodes_ca4fe9o17_DompabloCasals__DS_w59koijcczkz_0
Name: calcium_ferrites_as_cathodes_ca4fe9o17
Authors: M. Elena Arroyo-de Dompablo, José Luis Casals
Elements: Ca, Fe, O
Number of Configurations: 345
Number of Elements: 3
Number of Atoms: 35,462
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:xk-sn
http://doi.org/10.1039/c9dt04688g

Download Dataset XYZ file Description: GAP-20 describes the properties of the bulk crystalline and amorphous phases, crystal surfaces, and defect structures with an accuracy approaching that of direct ab initio simulation, but at a significantly reduced cost. The final potential is fitted to reference data computed using the optB88-vdW density functional theory (DFT) functional.

ColabFit ID: Carbon_GAP_JCP_2020_RoweDeringerGasparottoCsanyiMichaelides__DS_on6fusdw5n8q_0
Name: Carbon_GAP_JCP_2020
Authors: Patrick Rowe, Volker L. Deringer, Piero Gasparotto, Gábor Csányi, Angelos Michaelides
Elements: C
Number of Configurations: 17,227
Number of Elements: 1
Number of Atoms: 1,312,457
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://www.repository.cam.ac.uk/handle/1810/307452
https://doi.org/10.1063/5.0005084

Download Dataset XYZ file Description: Training data generated for GAP-20. GAP-20 describes the properties of the bulk crystalline and amorphous phases, crystal surfaces, and defect structures with an accuracy approaching that of direct ab initio simulation, but at a significantly reduced cost. The final potential is fitted to reference data computed using the optB88-vdW density functional theory (DFT) functional.

ColabFit ID: Carbon_GAP_JCP_2020_train_RoweDeringerGasparottoCsanyiMichaelides__DS_sz9me9r61581_0
Name: Carbon_GAP_JCP_2020_train
Authors: Patrick Rowe, Volker L. Deringer, Piero Gasparotto, Gábor Csányi, Angelos Michaelides
Elements: C
Number of Configurations: 6,088
Number of Elements: 1
Number of Atoms: 400,275
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://www.repository.cam.ac.uk/handle/1810/307452
https://doi.org/10.1063/5.0005084

Download Dataset XYZ file Description: Carolina Materials contains structures used to train several machine learning models for the efficient generation of hypothetical inorganic materials. The database is built using structures from OQMD, Materials Project and ICSD, as well as ML generated structures validated by DFT.

ColabFit ID: Carolina_Materials_ZhaoAl-FahdiHuSiriwardaneSongNasiriHu__DS_r2r8k3fyb6ny_0
Name: Carolina_Materials
Authors: Yong Zhao, Mohammed Al-Fahdi, Ming Hu, Edirisuriya M. D. Siriwardane, Yuqi Song, Alireza Nasiri, Jianjun Hu
Elements: Ir, Li, S, Te, Bi, Ga, I, Br, F, Sn, Mg, Rh, Si, Ta, Fe, V, Mn, Ni, Tc, Tl, Al, Ba, Zn, Zr, K, Pb, Se, B, C, Pt, Sb, O, W, Y, Ag, Ge, P, Ti, Cu, Pd, Au, N, As, Sr, Cr, Hg, Sc, Hf, Be, Na, Cd, Nb, Rb, Ru, Os, Co, Ca, Re, Mo, Cl, H, Cs, In, Po
Number of Configurations: 214,280
Number of Elements: 64
Number of Atoms: 3,168,502
Property Types: formation-energy

Links:
https://zenodo.org/records/8381476
https://doi.org/10.1002/advs.202100566
http://www.carolinamatdb.org/
https://github.com/IntelLabs/matsciml

Download Dataset XYZ file Description: Data from the publication "Enlisting Potential Cathode Materials for Rechargeable Ca Batteries". The development of rechargeable batteries based on a Ca metal anode demands the identification of suitable cathode materials. This work investigates the potential application of a variety of compounds, which are selected from the In-organic Crystal Structural Database (ICSD) considering 3d-transition metal oxysulphides, pyrophosphates, silicates, nitrides, and phosphates with a maximum of four different chemical elements in their composition. Cathode perfor-mance of CaFeSO, CaCoSO, CaNiN, Ca3MnN3, Ca2Fe(Si2O7), CaM(P2O7) (M = V, Cr, Mn, Fe, Co), CaV2(P2O7)2, Ca(VO)2(PO4)2 and α-VOPO4 is evaluated throughout the calculation of operation voltages, volume changes associated to the redox reaction and mobility of Ca2+ ions. Some materials exhibit attractive specific capacities and intercalation voltages combined with energy barriers for Ca migration around 1 eV (CaFeSO, Ca2FeSi2O7 and CaV2(P2O7)2). Based on the DFT results, αI-VOPO4 is identified as a potential Ca-cathode with a maximum theoretical specific capacity of 312 mAh/g, an average intercalation voltage of 2.8 V and calculated energy barriers for Ca migration below 0.65 eV (GGA functional).

ColabFit ID: cathode_materials_for_rechargeable_Ca_batteries_CM2021_DompabloCasals__DS_s00e64z80ujy_0
Name: cathode_materials_for_rechargeable_Ca_batteries_CM2021
Authors: M. Elena Arroyo-de Dompablo, Jose Luis Casals
Elements: N, O, Ca, V, Ni, S, Fe, Si, P, Mn, Co
Number of Configurations: 10,840
Number of Elements: 11
Number of Atoms: 1,034,770
Property Types: atomic-forces, potential-energy, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:3n-e8
http://doi.org/10.1038/s41598-019-46002-4

Download Dataset XYZ file Description: This dataset contains DFT calculations that were carried out in conjunction with experimental investigation of a cationic phenoxyimine yttrium complex as an isoprene polimerization catalyst. Calculations were performed using the Gaussian 09 D.01 suite of programs.Electronic structure calculations were performed at the DFT level using the B3PW91 functional. The Stuttgart-Cologne small-core quasi-relativistic pseudopotential ECP28MWB and its available basis set including up to the g function were used to describe yttrium. Similarly, silicon and phosphorus were represented by a Stuttgart-Dresden-Bonn pseudopotential along with the related basis set augmented by a d function of polarization (αd(P) = 0.387 and αd(Si) = 0.284). Other atoms were described by a polarized all-electron triple-ζ 6-311G(d,p) basis set. Bulk solvent effect of toluene or THF was simulated using the SMD continuum model. The Grimme empirical correction with the original D3 damping function was used to include the dispersion correction as a single-point calculation. Transition-state optimization was followed by frequency calculations to characterize the stationary point. Intrinsic reaction coordinate calculations were performed to confirm the connectivity of the transition states. Gibbs energies were estimated within the harmonic oscillator approximation and estimated at 298 K and 1 atm.

ColabFit ID: Cationic_phenoxyimine_complexes_of_yttrium_OswaldVerrieuxBreuilOlivier-BourbigouThuilliezVaultierTaoufikPerrinBoisson__DS_y6bp7td4dle0_0
Name: Cationic_phenoxyimine_complexes_of_yttrium
Authors: Alexis D. Oswald, Ludmilla Verrieux, Pierre-Alain R. Breuil, Hélène Olivier-Bourbigou, Julien Thuilliez, Florent Vaultier, Mostafa Taoufik, Lionel Perrin, Christophe Boisson
Elements: C, H, N, O, Y, Al, Si, B, F
Number of Configurations: 109
Number of Elements: 9
Number of Atoms: 9,074
Property Types: potential-energy

Links:
https://doi.org/10.1021/acs.organomet.2c00238.s001
https://doi.org/10.1021/acs.organomet.2c00238

Download Dataset XYZ file Description: Configurations from a cG-SchNet trained on a subset of the QM9dataset. Model was trained with the intention of providing molecules withspecified functional groups or motifs, relying on sampling of molecularfingerprint data. Relaxation data for the generated molecules is computedusing ORCA software. Configuration sets include raw data fromcG-SchNet-generated configurations, with models trained on several differenttypes of target data and DFT relaxation data as a separate configurationset. Includes approximately 80,000 configurations.

ColabFit ID: cG-SchNet_GebauerGasteggerHessmannMullerSchutt__DS_xzaglubh0trq_0
Name: cG-SchNet
Authors: Niklas W.A. Gebauer, Michael Gastegger, Stefaan S.P. Hessmann, Klaus-Robert Müller, Kristof T. Schütt
Elements: C, H, N, O, F
Number of Configurations: 79,772
Number of Elements: 5
Number of Atoms: 1,467,492
Property Types: potential-energy

Links:
https://github.com/atomistic-machine-learning/cG-SchNet/
https://doi.org/10.1038/s41467-022-28526-y

Download Dataset XYZ file Description: Training simulations from CGM-MLP_natcomm2023 of carbon deposition on a Cr surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.

ColabFit ID: CGM-MLP_natcomm2023_Cr-C_deposition_ZhangYiLaiPengLi__DS_392q5d027yja_0
Name: CGM-MLP_natcomm2023_Cr-C_deposition
Authors: Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Elements: C, Cr
Number of Configurations: 1,192
Number of Elements: 2
Number of Atoms: 298,114
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/sjtudizhang/CGM-MLP
https://doi.org/10.1038/s41467-023-44525-z

Download Dataset XYZ file Description: Training simulations from CGM-MLP_natcomm2023 of carbon deposition on a Cu surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.

ColabFit ID: CGM-MLP_natcomm2023_Cu-C_deposition_ZhangYiLaiPengLi__DS_vgy50b4qz4p7_0
Name: CGM-MLP_natcomm2023_Cu-C_deposition
Authors: Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Elements: C, Cu
Number of Configurations: 1,177
Number of Elements: 2
Number of Atoms: 204,591
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/sjtudizhang/CGM-MLP
https://doi.org/10.1038/s41467-023-44525-z

Download Dataset XYZ file Description: Training simulations from CGM-MLP_natcomm2023 of carbon on a Cu metal surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.

ColabFit ID: CGM-MLP_natcomm2023_Cu-C_metal_surface_ZhangYiLaiPengLi__DS_mo7q9ajg1lw8_0
Name: CGM-MLP_natcomm2023_Cu-C_metal_surface
Authors: Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Elements: C, Cu
Number of Configurations: 520
Number of Elements: 2
Number of Atoms: 122,294
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/sjtudizhang/CGM-MLP
https://doi.org/10.1038/s41467-023-44525-z

Download Dataset XYZ file Description: Training simulations from CGM-MLP_natcomm2023 of carbon on an oxygen-contaminated Cu surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.

ColabFit ID: CGM-MLP_natcomm2023_Cu-C-O_ZhangYiLaiPengLi__DS_xho213jy5pf9_0
Name: CGM-MLP_natcomm2023_Cu-C-O
Authors: Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Elements: C, Cu, O
Number of Configurations: 1,717
Number of Elements: 3
Number of Atoms: 387,151
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/sjtudizhang/CGM-MLP
https://doi.org/10.1038/s41467-023-44525-z

Download Dataset XYZ file Description: Training simulations from CGM-MLP_natcomm2023 of carbon deposition on a Cu surface. This appears similar to CGM-MLP_natcomm2023_CU-C_deposition, as there are no O atoms present in this set. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.

ColabFit ID: CGM-MLP_natcomm2023_Cu-C-O_deposition_ZhangYiLaiPengLi__DS_xzo1tvni8bay_0
Name: CGM-MLP_natcomm2023_Cu-C-O_deposition
Authors: Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Elements: C, Cu
Number of Configurations: 1,694
Number of Elements: 2
Number of Atoms: 326,328
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/sjtudizhang/CGM-MLP
https://doi.org/10.1038/s41467-023-44525-z

Download Dataset XYZ file Description: Carbon_GAP_20 dataset from CGM-MLP_natcomm2023. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.

ColabFit ID: CGM-MLP_natcomm2023_GAP_20_ZhangYiLaiPengLi__DS_qslbmhe4bcgg_0
Name: CGM-MLP_natcomm2023_GAP_20
Authors: Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Elements: C, Cu
Number of Configurations: 6,178
Number of Elements: 2
Number of Atoms: 400,485
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/sjtudizhang/CGM-MLP
https://doi.org/10.1038/s41467-023-44525-z

Download Dataset XYZ file Description: 493 structures available from the GAP-20 database, excluding any structures present in the training set. This dataset was one of the datasets used in testing screening parameters during the process of producing an active learning dataset for Cu-C interactions for the purposes of exploring substrate-catalyzed deposition as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu(111) surface.

ColabFit ID: CGM-MLP_natcomm2023_screening_amorphous_carbon_test_ZhangYiLaiPengLi__DS_wt0m3lxcdy52_0
Name: CGM-MLP_natcomm2023_screening_amorphous_carbon_test
Authors: Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Elements: C
Number of Configurations: 494
Number of Elements: 1
Number of Atoms: 32,279
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/sjtudizhang/CGM-MLP
https://doi.org/10.1038/s41467-023-44525-z

Download Dataset XYZ file Description: 2558 structures selected from the GAP-20 database. This dataset was one of the datasets used in testing screening parameters during the process of producing an active learning dataset for Cu-C interactions for the purposes of exploring substrate-catalyzed deposition as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu(111) surface.

ColabFit ID: CGM-MLP_natcomm2023_screening_amorphous_carbon_train_ZhangYiLaiPengLi__DS_taxwl7jo4f8a_0
Name: CGM-MLP_natcomm2023_screening_amorphous_carbon_train
Authors: Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Elements: C
Number of Configurations: 2,559
Number of Elements: 1
Number of Atoms: 168,191
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/sjtudizhang/CGM-MLP
https://doi.org/10.1038/s41467-023-44525-z

Download Dataset XYZ file Description: 192 structures were uniformly selected from the AIMD simulation, excluding any structures that are part of the training set. This dataset was one of the datasets used in testing screening parameters during the process of producing an active learning dataset for Cu-C interactions for the purposes of exploring substrate-catalyzed deposition as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu(111) surface.

ColabFit ID: CGM-MLP_natcomm2023_screening_carbon-cluster@Cu_test_ZhangYiLaiPengLi__DS_gkumjkncy8ft_0
Name: CGM-MLP_natcomm2023_screening_carbon-cluster@Cu_test
Authors: Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Elements: C, Cu
Number of Configurations: 193
Number of Elements: 2
Number of Atoms: 38,004
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/sjtudizhang/CGM-MLP
https://doi.org/10.1038/s41467-023-44525-z

Download Dataset XYZ file Description: 588 structures selected from the AIMD simulation of the Cu(111) slab, including both the C1-C18 clusters on the Cu(111) slab. This dataset was one of the datasets used in testing screening parameters during the process of producing an active learning dataset for Cu-C interactions for the purposes of exploring substrate-catalyzed deposition as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu(111) surface.

ColabFit ID: CGM-MLP_natcomm2023_screening_carbon-cluster@Cu_train_ZhangYiLaiPengLi__DS_6sjhg1f8fv5j_0
Name: CGM-MLP_natcomm2023_screening_carbon-cluster@Cu_train
Authors: Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Elements: C, Cu
Number of Configurations: 588
Number of Elements: 2
Number of Atoms: 115,460
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/sjtudizhang/CGM-MLP
https://doi.org/10.1038/s41467-023-44525-z

Download Dataset XYZ file Description: 468 structures uniformly selected from the MD/tfMC simulation, excluding any structures that are part of the training set. This dataset was one of the datasets used in testing screening parameters during the process of producing an active learning dataset for Cu-C interactions for the purposes of exploring substrate-catalyzed deposition as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu(111) surface.

ColabFit ID: CGM-MLP_natcomm2023_screening_deposited-carbon@Cu_test_ZhangYiLaiPengLi__DS_5r9dvov8nq7c_0
Name: CGM-MLP_natcomm2023_screening_deposited-carbon@Cu_test
Authors: Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Elements: C, Cu
Number of Configurations: 469
Number of Elements: 2
Number of Atoms: 156,312
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/sjtudizhang/CGM-MLP
https://doi.org/10.1038/s41467-023-44525-z

Download Dataset XYZ file Description: 1090 structures uniformly selected from the MD/tfMC simulation during the training process of CGM-MLPs. This dataset was one of the datasets used in testing screening parameters during the process of producing an active learning dataset for Cu-C interactions for the purposes of exploring substrate-catalyzed deposition as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu(111) surface.

ColabFit ID: CGM-MLP_natcomm2023_screening_deposited-carbon@Cu_train_ZhangYiLaiPengLi__DS_j6w4ru800ukq_0
Name: CGM-MLP_natcomm2023_screening_deposited-carbon@Cu_train
Authors: Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Elements: C, Cu
Number of Configurations: 1,091
Number of Elements: 2
Number of Atoms: 362,898
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/sjtudizhang/CGM-MLP
https://doi.org/10.1038/s41467-023-44525-z

Download Dataset XYZ file Description: 40 graphite structures with different lattice constants ranging from 2.0 to 3.2 Å, with a 0.03 Å increment. This dataset was one of the datasets used in testing screening parameters during the process of producing an active learning dataset for Cu-C interactions for the purposes of exploring substrate-catalyzed deposition as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu(111) surface.

ColabFit ID: CGM-MLP_natcomm2023_screening_graphite_train_ZhangYiLaiPengLi__DS_jasbxoigo7r4_0
Name: CGM-MLP_natcomm2023_screening_graphite_train
Authors: Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Elements: C
Number of Configurations: 41
Number of Elements: 1
Number of Atoms: 1,968
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/sjtudizhang/CGM-MLP
https://doi.org/10.1038/s41467-023-44525-z

Download Dataset XYZ file Description: Training simulations from CGM-MLP_natcomm2023 of carbon deposition on a Ti surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.

ColabFit ID: CGM-MLP_natcomm2023_Ti-C_deposition_ZhangYiLaiPengLi__DS_8frq3jczb0dt_0
Name: CGM-MLP_natcomm2023_Ti-C_deposition
Authors: Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Elements: C, Ti
Number of Configurations: 1,309
Number of Elements: 2
Number of Atoms: 259,636
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/sjtudizhang/CGM-MLP
https://doi.org/10.1038/s41467-023-44525-z

Download Dataset XYZ file Description: Test configurations from the 'random' split of Chig-AIMD. This dataset covers the conformational space of chignolin with DFT-level precision. We sequentially applied replica exchange molecular dynamics (REMD), conventional MD, and ab initio MD (AIMD) simulations on a 10 amino acid protein, Chignolin, and finally collected 2 million biomolecule structures with quantum level energy and force records.

ColabFit ID: Chig-AIMD_random_test_WangHeLiShaoLiu__DS_e08dhew7z0r6_0
Name: Chig-AIMD_random_test
Authors: Tong Wang, Xinheng He, Mingyu Li, Bin Shao, Tie-Yan Liu
Elements: C, H, N, O
Number of Configurations: 199,000
Number of Elements: 4
Number of Atoms: 33,034,000
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1038/s41597-023-02465-9
https://doi.org/10.6084/m9.figshare.22786730.v4

Download Dataset XYZ file Description: Training configurations from the 'random' split of Chig-AIMD. This dataset covers the conformational space of chignolin with DFT-level precision. We sequentially applied replica exchange molecular dynamics (REMD), conventional MD, and ab initio MD (AIMD) simulations on a 10 amino acid protein, Chignolin, and finally collected 2 million biomolecule structures with quantum level energy and force records.

ColabFit ID: Chig-AIMD_random_train_WangHeLiShaoLiu__DS_dsikv10na4f8_0
Name: Chig-AIMD_random_train
Authors: Tong Wang, Xinheng He, Mingyu Li, Bin Shao, Tie-Yan Liu
Elements: C, H, N, O
Number of Configurations: 1,592,800
Number of Elements: 4
Number of Atoms: 264,404,800
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1038/s41597-023-02465-9
https://doi.org/10.6084/m9.figshare.22786730.v4

Download Dataset XYZ file Description: Validation configurations from the 'random' split of Chig-AIMD. This dataset covers the conformational space of chignolin with DFT-level precision. We sequentially applied replica exchange molecular dynamics (REMD), conventional MD, and ab initio MD (AIMD) simulations on a 10 amino acid protein, Chignolin, and finally collected 2 million biomolecule structures with quantum level energy and force records.

ColabFit ID: Chig-AIMD_random_val_WangHeLiShaoLiu__DS_u0ggh1iie0uc_0
Name: Chig-AIMD_random_val
Authors: Tong Wang, Xinheng He, Mingyu Li, Bin Shao, Tie-Yan Liu
Elements: C, H, N, O
Number of Configurations: 199,000
Number of Elements: 4
Number of Atoms: 33,034,000
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1038/s41597-023-02465-9
https://doi.org/10.6084/m9.figshare.22786730.v4

Download Dataset XYZ file Description: Test configurations from the 'scaffold' split of Chig-AIMD. This dataset covers the conformational space of chignolin with DFT-level precision. We sequentially applied replica exchange molecular dynamics (REMD), conventional MD, and ab initio MD (AIMD) simulations on a 10 amino acid protein, Chignolin, and finally collected 2 million biomolecule structures with quantum level energy and force records.

ColabFit ID: Chig-AIMD_scaffold_test_WangHeLiShaoLiu__DS_zmzkyvep8l55_0
Name: Chig-AIMD_scaffold_test
Authors: Tong Wang, Xinheng He, Mingyu Li, Bin Shao, Tie-Yan Liu
Elements: C, H, N, O
Number of Configurations: 199,000
Number of Elements: 4
Number of Atoms: 33,034,000
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1038/s41597-023-02465-9
https://doi.org/10.6084/m9.figshare.22786730.v4

Download Dataset XYZ file Description: Training configurations from the 'scaffold' split of Chig-AIMD. This dataset covers the conformational space of chignolin with DFT-level precision. We sequentially applied replica exchange molecular dynamics (REMD), conventional MD, and ab initio MD (AIMD) simulations on a 10 amino acid protein, Chignolin, and finally collected 2 million biomolecule structures with quantum level energy and force records.

ColabFit ID: Chig-AIMD_scaffold_train_WangHeLiShaoLiu__DS_7puixss6qd61_0
Name: Chig-AIMD_scaffold_train
Authors: Tong Wang, Xinheng He, Mingyu Li, Bin Shao, Tie-Yan Liu
Elements: C, H, N, O
Number of Configurations: 1,592,800
Number of Elements: 4
Number of Atoms: 264,404,800
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1038/s41597-023-02465-9
https://doi.org/10.6084/m9.figshare.22786730.v4

Download Dataset XYZ file Description: Validation configurations from the 'scaffold' split of Chig-AIMD. This dataset covers the conformational space of chignolin with DFT-level precision. We sequentially applied replica exchange molecular dynamics (REMD), conventional MD, and ab initio MD (AIMD) simulations on a 10 amino acid protein, Chignolin, and finally collected 2 million biomolecule structures with quantum level energy and force records.

ColabFit ID: Chig-AIMD_scaffold_val_WangHeLiShaoLiu__DS_mzz13lim5qfi_0
Name: Chig-AIMD_scaffold_val
Authors: Tong Wang, Xinheng He, Mingyu Li, Bin Shao, Tie-Yan Liu
Elements: C, H, N, O
Number of Configurations: 199,000
Number of Elements: 4
Number of Atoms: 33,034,000
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1038/s41597-023-02465-9
https://doi.org/10.6084/m9.figshare.22786730.v4

Download Dataset XYZ file Description: This dataset of molecular structures was extracted, using the NOMAD API, from all available structures in the NOMAD Archive that only include C, H, O, and N. This dataset consists of 50.42% H, 30.41% C, 10.36% N, and 8.81% O and includes 96 804 atomic environments in 5217 structures.

ColabFit ID: CHON_JCP_2020_OnatOrtnerKermode__DS_3td9plyix4ib_0
Name: CHON_JCP_2020
Authors: Berk Onat, Christoph Ortner, James R. Kermode
Elements: C, H, N, O
Number of Configurations: 5,216
Number of Elements: 4
Number of Atoms: 96,736
Property Types: potential-energy, free-energy

Links:
https://github.com/DescriptorZoo/sensitivity-dimensionality-results/tree/master/datasets
https://doi.org/10.1063/5.0016005

Download Dataset XYZ file Description: This dataset contains dimer molecules of Co(II) with potential energy calculations for structures with ferromagnetic and antiferromagnetic spin configurations. Calculations were carried out in Gaussian 16 with the PBE exchange-correlation functional and 6-31+G* basis set. All molecules contain the same atomic core region, consisting of the tetrahedral and octahedral Co centers and the three PO2R2 bridging ligands. The ligand exchange provides a broad range of exchange energies (ΔEJ), from +50 to -200 meV, with 80% of the ligands yielding ΔEJ < 10 meV.

ColabFit ID: Co_dimer_JPCA_2022_RenFonsecaPerryChengZhangHennig__DS_uwqpfuuj6utw_0
Name: Co_dimer_JPCA_2022
Authors: Sijin Ren, Eric Fonseca, William Perry, Hai-Ping Cheng, Xiao-Guang Zhang, Richard Hennig
Elements: C, Co, H, N, O, P, S, Cl
Number of Configurations: 2,162
Number of Elements: 8
Number of Atoms: 188,438
Property Types: potential-energy

Links:
https://doi.org/10.24435/materialscloud:pe-zv
https://doi.org/10.1021/acs.jpca.1c08950

Download Dataset XYZ file Description: Training data only from the Co_dimer_JPCA_2022 dataset. This dataset contains dimer molecules of Co(II) with potential energy calculations for structures with ferromagnetic and antiferromagnetic spin configurations. Calculations were carried out in Gaussian 16 with the PBE exchange-correlation functional and 6-31+G* basis set. All molecules contain the same atomic core region, consisting of the tetrahedral and octahedral Co centers and the three PO2R2 bridging ligands. The ligand exchange provides a broad range of exchange energies (ΔEJ), from +50 to -200 meV, with 80% of the ligands yielding ΔEJ < 10 meV.

ColabFit ID: Co_dimer_JPCA_2022_train_RenFonsecaPerryChengZhangHennig__DS_doo9ltj3vsnu_0
Name: Co_dimer_JPCA_2022_train
Authors: Sijin Ren, Eric Fonseca, William Perry, Hai-Ping Cheng, Xiao-Guang Zhang, Richard Hennig
Elements: C, Co, H, N, O, P, S, Cl
Number of Configurations: 1,798
Number of Elements: 8
Number of Atoms: 154,882
Property Types: potential-energy

Links:
https://doi.org/10.24435/materialscloud:pe-zv
https://doi.org/10.1021/acs.jpca.1c08950

Download Dataset XYZ file Description: This dataset contains data from eight AIMD simulations run in VASP to study electrochemical *CO-*CO coupling -- coupling of two *CO molecules -- at the liquid water-Cu(100) interface.

ColabFit ID: Co-Co_coupling_at_liquid_water-Cu(100)_interfaces_JC2021_KristoffersenChan__DS_3a3auhtmkv45_0
Name: Co-Co_coupling_at_liquid_water-Cu(100)_interfaces_JC2021
Authors: Henrik H. Kristoffersen, Karen Chan
Elements: H, Cu, Cs, C, Li, O
Number of Configurations: 1,671,203
Number of Elements: 6
Number of Atoms: 226,264,514
Property Types: atomic-forces, potential-energy, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:p9-q7
https://doi.org/10.1016/j.jcat.2021.02.023

Download Dataset XYZ file Description: The dataset for "Origin of high strength in the CoCrFeNiPd high-entropy alloy", containing DFT-calculated values of the high-entropy alloy CoCrFeNiPd, created to explore the reasons behind experimental findings of the increased strength CoCrFeNiPd in comparison to CoCrFeNi.

ColabFit ID: CoCrFeNiPd_MRL2020_YinCurtin__DS_4w9gf8dxzskh_0
Name: CoCrFeNiPd_MRL2020
Authors: Binglun Yin, W. A. Curtin
Elements: Co, Cr, Fe, Ni, Pd
Number of Configurations: 116
Number of Elements: 5
Number of Atoms: 8,552
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:2020.0045/v1
https://doi.org/10.24435/materialscloud:2020.0045/v1

Download Dataset XYZ file Description: Training and simulation data from machine learning force field model applied to steps of the hydrogenation of carbon dioxide to methanol over an indium oxide catalyst, with and without platinum doping.

ColabFit ID: COHInPt_schaaf_2023_SchaafFakoDeSchaferCsanyi__DS_l9f0rjjqfd67_0
Name: COHInPt_schaaf_2023
Authors: Lars Schaaf, Edvin Fako, Sandip De, Ansgar Schafer, Gabor Csanyi
Elements: C, H, In, O, Pt
Number of Configurations: 1,994
Number of Elements: 5
Number of Atoms: 163,746
Property Types: free-energy, atomic-forces

Links:
https://doi.org/10.5281/zenodo.8268726
https://doi.org/10.48550/arXiv.2301.09931

Download Dataset XYZ file Description: Test set from COLL. Consists of configurations taken from molecular collisions of different small organic molecules. Energies and forces for 140,000 random snapshots taken from these trajectories were recomputed with density functional theory (DFT). These calculations were performed with the revPBE functional and def2-TZVP basis, including D3 dispersion corrections

ColabFit ID: COLL_test_GasteigerGiriMargrafGunnemann__DS_anwiyxoek66t_0
Name: COLL_test
Authors: Johannes Gasteiger, Shankari Giri, Johannes T. Margraf, Stephan Günnemann
Elements: C, H, O
Number of Configurations: 9,480
Number of Elements: 3
Number of Atoms: 97,886
Property Types: potential-energy, atomization-energy, atomic-forces

Links:
https://doi.org/10.6084/m9.figshare.13289165.v1
https://doi.org/10.48550/arXiv.2011.14115

Download Dataset XYZ file Description: Training set from COLL. Consists of configurations taken from molecular collisions of different small organic molecules. Energies and forces for 140,000 random snapshots taken from these trajectories were recomputed with density functional theory (DFT). These calculations were performed with the revPBE functional and def2-TZVP basis, including D3 dispersion corrections

ColabFit ID: COLL_train_GasteigerGiriMargrafGunnemann__DS_cifhpgzw3ckj_0
Name: COLL_train
Authors: Johannes Gasteiger, Shankari Giri, Johannes T. Margraf, Stephan Günnemann
Elements: C, H, O
Number of Configurations: 120,000
Number of Elements: 3
Number of Atoms: 1,225,350
Property Types: potential-energy, atomization-energy, atomic-forces

Links:
https://doi.org/10.6084/m9.figshare.13289165.v1
https://doi.org/10.48550/arXiv.2011.14115

Download Dataset XYZ file Description: Validation set from COLL. Consists of configurations taken from molecular collisions of different small organic molecules. Energies and forces for 140,000 random snapshots taken from these trajectories were recomputed with density functional theory (DFT). These calculations were performed with the revPBE functional and def2-TZVP basis, including D3 dispersion corrections

ColabFit ID: COLL_validation_GasteigerGiriMargrafGunnemann__DS_1y25o4zvyfm0_0
Name: COLL_validation
Authors: Johannes Gasteiger, Shankari Giri, Johannes T. Margraf, Stephan Günnemann
Elements: C, H, O
Number of Configurations: 10,000
Number of Elements: 3
Number of Atoms: 101,847
Property Types: potential-energy, atomization-energy, atomic-forces

Links:
https://doi.org/10.6084/m9.figshare.13289165.v1
https://doi.org/10.48550/arXiv.2011.14115

Download Dataset XYZ file Description: DFT-optimized geometries and properties for Li-S electrolytes. These make up the Computational Database for Li-S Batteries (ComBat), calculated using Gaussian 16 at the B3LYP/6-31+G* level of theory.

ColabFit ID: ComBat_AtwiBlissMakeevRajput__DS_xs942mj0c3dx_0
Name: ComBat
Authors: Rasha Atwi, Matthew Bliss, Maxim Makeev, Nav Nidhi Rajput
Elements: C, F, H, Li, N, O, S, P, Si
Number of Configurations: 230
Number of Elements: 9
Number of Atoms: 5,662
Property Types: potential-energy

Links:
https://github.com/rashatwi/combat/
https://doi.org/10.1038/s41598-022-20009-w

Download Dataset XYZ file Description: COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.

ColabFit ID: COMP6v2-B973c-def2mTZVP_HuddlestonZubatyukSmithRoitbergIsayevPickeringDevereuxBarros__DS_0dbwoxq96gga_0
Name: COMP6v2-B973c-def2mTZVP
Authors: Kate Huddleston, Roman Zubatyuk, Justin Smith, Adrian Roitberg, Olexandr Isayev, Ignacio Pickering, Christian Devereux, Kipton Barros
Elements: N, C, Cl, H, F, S, O
Number of Configurations: 156,330
Number of Elements: 7
Number of Atoms: 3,786,071
Property Types: atomic-forces, potential-energy

Links:
https://doi.org/10.5281/zenodo.10126157
https://doi.org/10.1021/acs.jctc.0c00121

Download Dataset XYZ file Description: COMP6v2-wB97MV-def2TZVPP is the portion of COMP6v2 calculated at the wB97MV/def2TZVPP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.

ColabFit ID: COMP6v2-wB97MV-def2TZVPP_HuddlestonZubatyukSmithRoitbergIsayevPickeringDevereuxBarros__DS_m6etjqhlu40m_0
Name: COMP6v2-wB97MV-def2TZVPP
Authors: Kate Huddleston, Roman Zubatyuk, Justin Smith, Adrian Roitberg, Olexandr Isayev, Ignacio Pickering, Christian Devereux, Kipton Barros
Elements: N, F, Cl, S, H, O, C
Number of Configurations: 156,369
Number of Elements: 7
Number of Atoms: 3,787,406
Property Types: potential-energy

Links:
https://doi.org/10.5281/zenodo.10126157
https://doi.org/10.1021/acs.jctc.0c00121

Download Dataset XYZ file Description: COMP6v2-wB97X-631Gd is the portion of COMP6v2 calculated at the wB97X/631Gd level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.

ColabFit ID: COMP6v2-wB97X-631Gd_HuddlestonZubatyukSmithRoitbergIsayevPickeringDevereuxBarros__DS_15m7etw1om8a_0
Name: COMP6v2-wB97X-631Gd
Authors: Kate Huddleston, Roman Zubatyuk, Justin Smith, Adrian Roitberg, Olexandr Isayev, Ignacio Pickering, Christian Devereux, Kipton Barros
Elements: Cl, N, S, C, F, O, H
Number of Configurations: 157,728
Number of Elements: 7
Number of Atoms: 3,897,978
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.5281/zenodo.10126157
https://doi.org/10.1021/acs.jctc.0c00121

Download Dataset XYZ file Description: This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 27,000 configurations that were bcc-like and close-packed (fcc, hcp, etc.) with 8 or fewer atoms in the unit cell and different concentrations of Co, Nb, and V.

ColabFit ID: CoNbV_CMS2019_GubaevPodryabinkinHartShapeev__DS_sn623uhg2d1b_0
Name: CoNbV_CMS2019
Authors: Konstantin Gubaev, Evgeny V. Podryabinkin, Gus L.W. Hart, Alexander V. Shapeev
Elements: Co, Nb, V
Number of Configurations: 383
Number of Elements: 3
Number of Atoms: 2,812
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data
https://doi.org/10.1016/j.commatsci.2018.09.031

Download Dataset XYZ file Description: Approximately 46,000 configurations of copper, including small and bulk structures, surfaces, interfaces, point defects, and randomly modified variants. Also includes structures with displaced or missing atoms.

ColabFit ID: Cu_FHI-aims_NPJCM_2021_LysogorskiyOordBochkarevMenonRinaldiHammerschmidtMrovecThompsonCsanyiOrtnerDrautz__DS_39v8vn61tlzl_0
Name: Cu_FHI-aims_NPJCM_2021
Authors: Yury Lysogorskiy, Cas van der Oord, Anton Bochkarev, Sarath Menon, Matteo Rinaldi, Thomas Hammerschmidt, Matous Mrovec, Aidan Thompson, Gábor Csányi, Christoph Ortner, Ralf Drautz
Elements: Cu
Number of Configurations: 46,583
Number of Elements: 1
Number of Atoms: 308,679
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.5281/zenodo.4734035
https://doi.org/10.1038/s41524-021-00559-9

Download Dataset XYZ file Description: This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included 40,000 unrelaxed configurations with BCC, FCC, and HCP lattices.

ColabFit ID: CuPd_CMS2019_GubaevPodryabinkinHartShapeev__DS_0ry6z1j8mi8c_0
Name: CuPd_CMS2019
Authors: Konstantin Gubaev, Evgeny V. Podryabinkin, Gus L.W. Hart, Alexander V. Shapeev
Elements: Cu, Pd
Number of Configurations: 522
Number of Elements: 2
Number of Atoms: 2,450
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data
https://doi.org/10.1016/j.commatsci.2018.09.031

Download Dataset XYZ file Description: Approximately 850 configurations of CoSb3 and Mg3Sb2 generated using a dual adaptive sampling (DAS) method for use with machine learning of interatomic potentials (MLIP).

ColabFit ID: DAS_MLIP_CoSb_MgSb_YangZhuDongWuYangZhang__DS_eyo7scxvrfq0_0
Name: DAS_MLIP_CoSb_MgSb
Authors: Hongliang Yang, Yifan Zhu, Erting Dong, Yabei Wu, Jiong Yang, Wenqing Zhang
Elements: Mg, Sb
Number of Configurations: 846
Number of Elements: 2
Number of Atoms: 247,744
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1103/PhysRevB.104.094310
https://doi.org/10.1103/PhysRevB.104.094310

Download Dataset XYZ file Description: This dataset comprises a training dataset for magnetic multi-component machine-learning potentials for Fe-Al systems, including different concentrations of Fe and Al (Al concentrations from 0%-50%), with fully equilibrated and perturbed atomic positions, lattice vectors and magnetic moments represented.

ColabFit ID: datasets_for_magnetic_MTP_NatSR2024_training_KotykhovGubaevHodappTantardiniShapeevNovikov__DS_mf8sn11cn6wa_0
Name: datasets_for_magnetic_MTP_NatSR2024_training
Authors: Alexey S. Kotykhov, Konstantin Gubaev, Max Hodapp, Christian Tantardini, Alexander V. Shapeev, Ivan S. Novikov
Elements: Al, Fe
Number of Configurations: 2,012
Number of Elements: 2
Number of Atoms: 11,440
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://gitlab.com/ivannovikov/datasets_for_magnetic_MTP
https://doi.org/10.1038/s41598-023-46951-x

Download Dataset XYZ file Description: This is the verification dataset (see companion training dataset: datasets_for_magnetic_MTP_NatSR2024_training) used in training a magnetic multi-component machine-learning potential for Fe-Al systems. The configurations from the verification set include different levels of magnetic moment perturbation than configurations from the training set. For this reason, the authors refer to this dataset as a "verification set", rather than a "validation set".

ColabFit ID: datasets_for_magnetic_MTP_NatSR2024_verification_KotykhovGubaevHodappTantardiniShapeevNovikov__DS_wu6xd9i8cf7i_0
Name: datasets_for_magnetic_MTP_NatSR2024_verification
Authors: Alexey S. Kotykhov, Konstantin Gubaev, Max Hodapp, Christian Tantardini, Alexander V. Shapeev, Ivan S. Novikov
Elements: Al, Fe
Number of Configurations: 336
Number of Elements: 2
Number of Atoms: 3,696
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://gitlab.com/ivannovikov/datasets_for_magnetic_MTP
https://doi.org/10.1038/s41598-023-46951-x

Download Dataset XYZ file Description: 127,000 configurations from a dataset used to benchmark and train a modified DeePMD model called DeepPot-SE, or Deep Potential - Smooth Edition

ColabFit ID: DeePMD_SE_ZhangHanWangSaidiCarE__DS_k85kj1kiekip_0
Name: DeePMD_SE
Authors: Linfeng Zhang, Jiequn Han, Han Wang, Wissam A. Saidi, Roberto Car, Weinan E
Elements: Mo, Pt, S, C, H, N, Si, Al, O, Ge, Co, Cr, Fe, Mn, Ni, Ti, Cu
Number of Configurations: 127,112
Number of Elements: 17
Number of Atoms: 26,278,380
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://www.aissquare.com/datasets/detail?pageType=datasets&name=deepmd-se-dataset
https://doi.org/10.48550/arXiv.1805.09003

Download Dataset XYZ file Description: This dataset contains pristine monolayer phosphorene as well as structures with monovacancies which were used to train an artificial neural network (ANN) for use with a high-dimensional neural network potentials molecular dynamics (HDNNP-MD) simulation. The publication investigates the mechanism and rates of the processes of defect diffusion, as well as monovacancy-to-divacancy defect coalescence.

ColabFit ID: defected_phosphorene_ACS_2023_KyvalaAngelettiFranchiniDellago__DS_k059wtxqsksu_0
Name: defected_phosphorene_ACS_2023
Authors: Lukáš Kývala, Andrea Angeletti, Cesare Franchini, Christoph Dellago
Elements: P
Number of Configurations: 5,091
Number of Elements: 1
Number of Atoms: 722,311
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.5281/zenodo.8421094
https://doi.org/10.1021/acs.jpcc.3c05713

Download Dataset XYZ file Description: Benzene test PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE_TS_benzene_test_KapilEngel__DS_23820czfaq24_0
Name: DFT_polymorphs_PNAS_2022_PBE_TS_benzene_test
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H
Number of Configurations: 1,000
Number of Elements: 2
Number of Atoms: 29,736
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Benzene training PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE_TS_benzene_train_KapilEngel__DS_3qi25f3sxkwr_0
Name: DFT_polymorphs_PNAS_2022_PBE_TS_benzene_train
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H
Number of Configurations: 55,000
Number of Elements: 2
Number of Atoms: 1,602,048
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Benzene validation PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE_TS_benzene_validation_KapilEngel__DS_8ou78o18mvfz_0
Name: DFT_polymorphs_PNAS_2022_PBE_TS_benzene_validation
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H
Number of Configurations: 1,000
Number of Elements: 2
Number of Atoms: 29,712
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Glycine test PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE_TS_glycine_test_KapilEngel__DS_qx4h8j4luk70_0
Name: DFT_polymorphs_PNAS_2022_PBE_TS_glycine_test
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H, N, O
Number of Configurations: 500
Number of Elements: 4
Number of Atoms: 17,710
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Glycine training PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE_TS_glycine_train_KapilEngel__DS_op9kvcm7ui6l_0
Name: DFT_polymorphs_PNAS_2022_PBE_TS_glycine_train
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H, N, O
Number of Configurations: 29,070
Number of Elements: 4
Number of Atoms: 952,650
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Glycine validation PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE_TS_glycine_validation_KapilEngel__DS_052bb0zufjaz_0
Name: DFT_polymorphs_PNAS_2022_PBE_TS_glycine_validation
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H, N, O
Number of Configurations: 500
Number of Elements: 4
Number of Atoms: 17,800
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Succinic acid test PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE_TS_succinic_acid_test_KapilEngel__DS_qmsg3kejmsy2_0
Name: DFT_polymorphs_PNAS_2022_PBE_TS_succinic_acid_test
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H, O
Number of Configurations: 500
Number of Elements: 3
Number of Atoms: 14,000
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Succinic acid training PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE_TS_succinic_acid_train_KapilEngel__DS_0u0k9ghrlkpx_0
Name: DFT_polymorphs_PNAS_2022_PBE_TS_succinic_acid_train
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H, O
Number of Configurations: 29,212
Number of Elements: 3
Number of Atoms: 817,936
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Succinic acid validation PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE_TS_succinic_acid_validation_KapilEngel__DS_nny0vpj1dy9s_0
Name: DFT_polymorphs_PNAS_2022_PBE_TS_succinic_acid_validation
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H, O
Number of Configurations: 500
Number of Elements: 3
Number of Atoms: 14,000
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Benzene test PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE0_MBD_benzene_test_KapilEngel__DS_ech19sh1koj4_0
Name: DFT_polymorphs_PNAS_2022_PBE0_MBD_benzene_test
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H
Number of Configurations: 200
Number of Elements: 2
Number of Atoms: 5,760
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Benzene training PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE0_MBD_benzene_train_KapilEngel__DS_ce4zytu7ph7e_0
Name: DFT_polymorphs_PNAS_2022_PBE0_MBD_benzene_train
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H
Number of Configurations: 1,800
Number of Elements: 2
Number of Atoms: 49,536
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Benzene validation PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE0_MBD_benzene_validation_KapilEngel__DS_xaq6jwha0a7o_0
Name: DFT_polymorphs_PNAS_2022_PBE0_MBD_benzene_validation
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H
Number of Configurations: 200
Number of Elements: 2
Number of Atoms: 6,072
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Glycine test PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE0_MBD_glycine_test_KapilEngel__DS_h3jymhr74qv4_0
Name: DFT_polymorphs_PNAS_2022_PBE0_MBD_glycine_test
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H, N, O
Number of Configurations: 200
Number of Elements: 4
Number of Atoms: 6,880
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Glycine training PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE0_MBD_glycine_train_KapilEngel__DS_b1n228x610dj_0
Name: DFT_polymorphs_PNAS_2022_PBE0_MBD_glycine_train
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H, N, O
Number of Configurations: 3,594
Number of Elements: 4
Number of Atoms: 109,940
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Glycine validation PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE0_MBD_glycine_validation_KapilEngel__DS_n1nilf7ralhq_0
Name: DFT_polymorphs_PNAS_2022_PBE0_MBD_glycine_validation
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H, N, O
Number of Configurations: 200
Number of Elements: 4
Number of Atoms: 7,120
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Succinic acid test PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE0_MBD_succinic_acid_test_KapilEngel__DS_vbjvssevrff5_0
Name: DFT_polymorphs_PNAS_2022_PBE0_MBD_succinic_acid_test
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H, O
Number of Configurations: 200
Number of Elements: 3
Number of Atoms: 5,600
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Succinic acid training PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE0_MBD_succinic_acid_train_KapilEngel__DS_pqmtyp5bxd46_0
Name: DFT_polymorphs_PNAS_2022_PBE0_MBD_succinic_acid_train
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H, O
Number of Configurations: 1,800
Number of Elements: 3
Number of Atoms: 50,400
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Succinic acid validation PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

ColabFit ID: DFT_polymorphs_PNAS_2022_PBE0_MBD_succinic_acid_validation_KapilEngel__DS_w18131j6r4oo_0
Name: DFT_polymorphs_PNAS_2022_PBE0_MBD_succinic_acid_validation
Authors: Venkat Kapil, Edgar A. Engel
Elements: C, H, O
Number of Configurations: 200
Number of Elements: 3
Number of Atoms: 5,600
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:vp-jf
https://doi.org/10.1073/pnas.2111769119
https://github.com/venkatkapil24/data_molecular_fluctuations

Download Dataset XYZ file Description: Structures from discrepencies_and_error_metrics_NPJ_2023 validation set, enhanced by inclusion of rare events. The full discrepencies_and_error_metrics_NPJ_2023 dataset includes the original mlearn_Si_train dataset, modified with the purpose of developing models with better diffusivity scores by replacing ~54% of the data with structures containing migrating interstitials. The enhanced validation set contains 50 total structures, consisting of 20 structures randomly selected from the 120 replaced structures of the original training dataset, 11 snapshots with vacancy rare events (RE) from AIMD simulations, and 19 snapshots with interstitial RE from AIMD simulations. We also construct interstitial-RE and vacancy-RE testing sets, each consisting of 100 snapshots of atomic configurations with a single migrating vacancy or interstitial, respectively, from AIMD simulations at 1230 K.

ColabFit ID: discrepencies_and_error_metrics_NPJ_2023_enhanced_validation_set_LiuHeMo__DS_q6e3bvq4y67a_0
Name: discrepencies_and_error_metrics_NPJ_2023_enhanced_validation_set
Authors: Yunsheng Liu, Xingfeng He, Yifei Mo
Elements: Si
Number of Configurations: 50
Number of Elements: 1
Number of Atoms: 3,198
Property Types: potential-energy, atomic-forces

Links:
https://github.com/mogroupumd/Silicon_MLIP_datasets
https://doi.org/10.1038/s41524-023-01123-3

Download Dataset XYZ file Description: Structures from discrepencies_and_error_metrics_NPJ_2023 training set, enhanced by inclusion of interstitials. The full discrepencies_and_error_metrics_NPJ_2023 dataset includes the original mlearn_Si_train dataset, modified with the purpose of developing models with better diffusivity scores by replacing ~54% of the data with structures containing migrating interstitials. The enhanced validation set contains 50 total structures, consisting of 20 structures randomly selected from the 120 replaced structures of the original training dataset, 11 snapshots with vacancy rare events (RE) from AIMD simulations, and 19 snapshots with interstitial RE from AIMD simulations. We also construct interstitial-RE and vacancy-RE testing sets, each consisting of 100 snapshots of atomic configurations with a single migrating vacancy or interstitial, respectively, from AIMD simulations at 1230 K.

ColabFit ID: discrepencies_and_error_metrics_NPJ_2023_interstitial_enhanced_training_set_LiuHeMo__DS_nublbp38wse0_0
Name: discrepencies_and_error_metrics_NPJ_2023_interstitial_enhanced_training_set
Authors: Yunsheng Liu, Xingfeng He, Yifei Mo
Elements: Si
Number of Configurations: 218
Number of Elements: 1
Number of Atoms: 13,629
Property Types: potential-energy, atomic-forces

Links:
https://github.com/mogroupumd/Silicon_MLIP_datasets
https://doi.org/10.1038/s41524-023-01123-3

Download Dataset XYZ file Description: Structures from discrepencies_and_error_metrics_NPJ_2023 test set; these include an interstitial. The full discrepencies_and_error_metrics_NPJ_2023 dataset includes the original mlearn_Si_train dataset, modified with the purpose of developing models with better diffusivity scores by replacing ~54% of the data with structures containing migrating interstitials. The enhanced validation set contains 50 total structures, consisting of 20 structures randomly selected from the 120 replaced structures of the original training dataset, 11 snapshots with vacancy rare events (RE) from AIMD simulations, and 19 snapshots with interstitial RE from AIMD simulations. We also construct interstitial-RE and vacancy-RE testing sets, each consisting of 100 snapshots of atomic configurations with a single migrating vacancy or interstitial, respectively, from AIMD simulations at 1230 K.

ColabFit ID: discrepencies_and_error_metrics_NPJ_2023_interstitial_re_testing_set_LiuHeMo__DS_dhe9aqs9q1wf_0
Name: discrepencies_and_error_metrics_NPJ_2023_interstitial_re_testing_set
Authors: Yunsheng Liu, Xingfeng He, Yifei Mo
Elements: Si
Number of Configurations: 100
Number of Elements: 1
Number of Atoms: 6,500
Property Types: potential-energy, atomic-forces

Links:
https://github.com/mogroupumd/Silicon_MLIP_datasets
https://doi.org/10.1038/s41524-023-01123-3

Download Dataset XYZ file Description: Structures from discrepencies_and_error_metrics_NPJ_2023 training set; includes some structures with vacancies. The full discrepencies_and_error_metrics_NPJ_2023 dataset includes the original mlearn_Si_train dataset, modified with the purpose of developing models with better diffusivity scores by replacing ~54% of the data with structures containing migrating interstitials. The enhanced validation set contains 50 total structures, consisting of 20 structures randomly selected from the 120 replaced structures of the original training dataset, 11 snapshots with vacancy rare events (RE) from AIMD simulations, and 19 snapshots with interstitial RE from AIMD simulations. We also construct interstitial-RE and vacancy-RE testing sets, each consisting of 100 snapshots of atomic configurations with a single migrating vacancy or interstitial, respectively, from AIMD simulations at 1230 K.

ColabFit ID: discrepencies_and_error_metrics_NPJ_2023_vacancy_enhanced_training_set_LiuHeMo__DS_qxd7wv9yabtp_0
Name: discrepencies_and_error_metrics_NPJ_2023_vacancy_enhanced_training_set
Authors: Yunsheng Liu, Xingfeng He, Yifei Mo
Elements: Si
Number of Configurations: 218
Number of Elements: 1
Number of Atoms: 13,389
Property Types: potential-energy, atomic-forces

Links:
https://github.com/mogroupumd/Silicon_MLIP_datasets
https://doi.org/10.1038/s41524-023-01123-3

Download Dataset XYZ file Description: Structures from discrepencies_and_error_metrics_NPJ_2023 test set; these include a single migrating vacancy. The full discrepencies_and_error_metrics_NPJ_2023 dataset includes the original mlearn_Si_train dataset, modified with the purpose of developing models with better diffusivity scores by replacing ~54% of the data with structures containing migrating interstitials. The enhanced validation set contains 50 total structures, consisting of 20 structures randomly selected from the 120 replaced structures of the original training dataset, 11 snapshots with vacancy rare events (RE) from AIMD simulations, and 19 snapshots with interstitial RE from AIMD simulations. We also construct interstitial-RE and vacancy-RE testing sets, each consisting of 100 snapshots of atomic configurations with a single migrating vacancy or interstitial, respectively, from AIMD simulations at 1230 K.

ColabFit ID: discrepencies_and_error_metrics_NPJ_2023_vacancy_re_testing_set_LiuHeMo__DS_la08goe2lz0g_0
Name: discrepencies_and_error_metrics_NPJ_2023_vacancy_re_testing_set
Authors: Yunsheng Liu, Xingfeng He, Yifei Mo
Elements: Si
Number of Configurations: 100
Number of Elements: 1
Number of Atoms: 6,300
Property Types: potential-energy, atomic-forces

Links:
https://github.com/mogroupumd/Silicon_MLIP_datasets
https://doi.org/10.1038/s41524-023-01123-3

Download Dataset XYZ file Description: Data from "On-the-fly assessment of diffusion barriers of disordered transition metal oxyfluorides using local descriptors". The dataset contains the result of 48 Nudged Elastic Band calculations of Li(2-x)VO2F diffusion barriers. The NEB was performed with VASP, using projector augmented-wave (PAW) method to describe electron-ion interaction. The disordered rock salt cells were created using a 3 x 4 x 4 supercell containing 96 atoms (in case of no vacancies). PBE is used as XC functional while a rotationally invariant Hubbard U correction was applied to the d orbital of V with a U value of 3.25 eV.

ColabFit ID: disordered_transition_metal_oxyfluorides_EA2021_ChangJorgensenLoftagerBhowmikLastraVegge__DS_k1k8iul6kgm2_0
Name: disordered_transition_metal_oxyfluorides_EA2021
Authors: Jin Hyun Chang, Peter Bjørn Jørgensen, Simon Loftager, Arghya Bhowmik, Juan María García Lastra, Tejs Vegge
Elements: Li, V, F, O
Number of Configurations: 233
Number of Elements: 4
Number of Atoms: 20,670
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:9v-3q
http://doi.org/10.1016/j.electacta.2021.138551

Download Dataset XYZ file Description: The test set from the doped CsPbI3 energetics dataset. This dataset was created to explore the effect of Cd and Pb substitutions on the structural stability of inorganic lead halide perovskite CsPbI3. CsPbI3 undergoes a direct to indirect band-gap phase transition at room temperature. The dataset contains configurations of CsPbI3 with low levels of Cd and Zn, which were used to train a GNN model to predict the energetics of structures with higher levels of substitutions.

ColabFit ID: doped_CsPbI3_energetics_test_EreminHumonenLazarevPushkarevBudennyy__DS_a2ftwj9b873a_0
Name: doped_CsPbI3_energetics_test
Authors: Roman A. Eremin, Innokentiy S. Humonen, Alexey A. Kazakov, Vladimir D. Lazarev, Anatoly P. Pushkarev, Semen A. Budennyy
Elements: Cd, Cs, I, Pb, Zn
Number of Configurations: 60
Number of Elements: 5
Number of Atoms: 9,600
Property Types: potential-energy, atomic-forces, formation-energy

Links:
https://github.com/AIRI-Institute/doped_CsPbI3_energetics
https://doi.org/10.1016/j.commatsci.2023.112672

Download Dataset XYZ file Description: The training + validation set from the doped CsPbI3 energetics dataset. This dataset was created to explore the effect of Cd and Pb substitutions on the structural stability of inorganic lead halide perovskite CsPbI3. CsPbI3 undergoes a direct to indirect band-gap phase transition at room temperature. The dataset contains configurations of CsPbI3 with low levels of Cd and Zn, which were used to train a GNN model to predict the energetics of structures with higher levels of substitutions.

ColabFit ID: doped_CsPbI3_energetics_train_validate_EreminHumonenLazarevPushkarevBudennyy__DS_doj9b688juif_0
Name: doped_CsPbI3_energetics_train_validate
Authors: Roman A. Eremin, Innokentiy S. Humonen, Alexey A. Kazakov, Vladimir D. Lazarev, Anatoly P. Pushkarev, Semen A. Budennyy
Elements: Cs, I, Pb, Zn, Cd
Number of Configurations: 142
Number of Elements: 5
Number of Atoms: 22,720
Property Types: potential-energy, atomic-forces, formation-energy

Links:
https://github.com/AIRI-Institute/doped_CsPbI3_energetics
https://doi.org/10.1016/j.commatsci.2023.112672

Download Dataset XYZ file Description: Approximately 15,000 configurations of copper used to demonstrate the DP-GEN data generator for PES machine learning models.

ColabFit ID: DP-GEN_Cu_ZhangWangChenZengZhangWangE__DS_101uk70asqhq_0
Name: DP-GEN_Cu
Authors: Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, Weinan E
Elements: Cu
Number of Configurations: 15,286
Number of Elements: 1
Number of Atoms: 297,597
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://www.aissquare.com/datasets/detail?pageType=datasets&name=Cu-dpgen
https://doi.org/10.1016/j.cpc.2020.107206

Download Dataset XYZ file Description: Dataset for "Analysis of minerals as electrode materials for Ca-based rechargeable batteries". Includes DFT structures of pyroxenes, garnet and carbonates. Dataset was produced to pursue identification of Ca-based high specific energy cathode materials.

ColabFit ID: electrode_materials_for_ca-based_rechargeable_batteries_DompabloCasals__DS_swl40hdnt479_0
Name: electrode_materials_for_ca-based_rechargeable_batteries
Authors: M. Elena Arroyo-de Dompablo, Jose Luis Casals
Elements: Ca, Mn, O, Si, C, Cr
Number of Configurations: 4,726
Number of Elements: 6
Number of Atoms: 550,074
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:3n-e8
http://doi.org/10.1038/s41598-019-46002-4

Download Dataset XYZ file Description: This iron nanoparticles database contains dimers; trimers; bcc, fcc, hexagonal close-packed (hcp), simple cubic, and diamond crystalline structures. A wide range of cell parameters, as well as rattled structures, bcc-fcc and bcc-hcp transitional structures, surface slabs cleaved from relaxed bulk structures, nanoparticles and liquid configurations are included. The energy, forces and virials for the atomic structures were computed at the DFT level of theory using VASP with the PBE functional and standard PAW pseudopotentials for Fe (with 8 valence electrons, 4s^23d^6). The kinetic energy cutoff for plane waves was set to 400 eV and the energy threshold for convergence was 10-7 eV. All the DFT calculations were carried out with spin polarization.

ColabFit ID: Fe_nanoparticles_PRB_2023_JanaCaro__DS_5h3810yhu4wj_0
Name: Fe_nanoparticles_PRB_2023
Authors: Richard Jana, Miguel A. Caro
Elements: Fe
Number of Configurations: 198
Number of Elements: 1
Number of Atoms: 20,097
Property Types: potential-energy, atomic-forces, free-energy, cauchy-stress

Links:
https://doi.org/10.5281/zenodo.7632315
https://doi.org/10.1103/PhysRevB.107.245421

Download Dataset XYZ file Description: Dataset for "Interplay between ferroelectricity and metallicity in BaTiO3", exploring properties of ferroelectric barium titanate (BaTiO3), including the effects of electron and hole doping. Includes configuration sets for unit cells and supercells of BaTiO3.

ColabFit ID: ferroelectricity_and_metallicity_in_BaTiO3_JMCC2021_MichelEssweinSpaldin__DS_1t2xs8bzygtp_0
Name: ferroelectricity_and_metallicity_in_BaTiO3_JMCC2021
Authors: Veronica F. Michel, Tobias Esswein, Nicola A. Spaldin
Elements: Ba, K, O, Ti, La, V, Al, Nb, Sc
Number of Configurations: 1,127
Number of Elements: 9
Number of Atoms: 19,125
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:f4-94
http://doi.org/10.1039/D1TC01868J

Download Dataset XYZ file Description: About 2,500 configurations of alpha-Fe used in the training and testing of a ML model with the goal of building magneto-elastic machine-learning interatomic potentials for large-scale spin-lattice dynamics simulations.

ColabFit ID: FitSNAP_Fe_NPJ_2021_NikolovWoodCangiMailletMarinicaThompsonDesjarlaisTranchida__DS_ej5u0tuycoph_0
Name: FitSNAP_Fe_NPJ_2021
Authors: Svetoslav Nikolov, Mitchell A. Wood, Attila Cangi, Jean-Bernard Maillet, Mihai-Cosmin Marinica, Aidan P. Thompson, Michael P. Desjarlais, Julien Tranchida
Elements: Fe
Number of Configurations: 2,517
Number of Elements: 1
Number of Atoms: 61,526
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/FitSNAP
https://doi.org/10.1038/s41524-021-00617-2

Download Dataset XYZ file Description: Configurations of azobenzene featuring a cis to trans thermal inversion through three channels: inversion, rotation, and rotation assisted by inversion; and configurations of glycine as a simpler comparison molecule. All calculations were performed in FHI-aims software using the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional with the Tkatchenko-Scheffler (TS) method to account for van der Waals (vdW) interactions. The azobenzene sets contain calculations from several different MD simulations, including two long simulations initialized at 300 K; short simulations (300 steps) initialized at 300 K and shorter (.5fs) timestep; four simulations, two starting from each of cis and trans isomer, at 750 K (initialized at 3000 K); and simulations at 50 K (initialized at 300 K). The glycine isomerization set was built using one MD simulation starting from each of two different minima. Initializatin and simulation temperature were 500 K.

ColabFit ID: flexible_molecules_JCP2021_Vassilev-GalindoFonsecaPoltavskyTkatchenko__DS_i23sbm1o45sj_0
Name: flexible_molecules_JCP2021
Authors: Valentin Vassilev-Galindo, Gregory Fonseca, Igor Poltavsky, Alexandre Tkatchenko
Elements: C, H, N, O
Number of Configurations: 69,182
Number of Elements: 4
Number of Atoms: 1,520,340
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.1063/5.0038516
https://doi.org/10.1063/5.0038516

Download Dataset XYZ file Description: Approximately 300,000 benchmarking configurations derived partly from the MD-17 and LiPS datasets, partly from original simulated water and alanine dipeptide configurations.

ColabFit ID: Forces_are_not_enough_FuWuWangXieKetenGomez-BombarelliJaakkola__DS_tku3ae1rtxiy_0
Name: Forces_are_not_enough
Authors: Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, Tommi Jaakkola
Elements: C, H, N, O, Li, P, S
Number of Configurations: 295,001
Number of Elements: 7
Number of Atoms: 23,735,083
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.5281/zenodo.7196767
https://doi.org/10.48550/arXiv.2210.07237
https://github.com/kyonofx/MDsim/

Download Dataset XYZ file Description: 133,855 configurations of stable small organic molecules composed of CHONF. A subset of GDB-17, with calculations of energies, dipole moment, polarizability and enthalpy. Calculations performed at B3LYP/6-31G(2df,p) level of theory.

ColabFit ID: GDB_9_nature_2014_RamakrishnanDralRuppLilienfeld__DS_2s5grfmdez1q_0
Name: GDB_9_nature_2014
Authors: Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld
Elements: C, H, O, N, F
Number of Configurations: 133,885
Number of Elements: 5
Number of Atoms: 2,407,753
Property Types: free-energy, potential-energy

Links:
https://doi.org/10.6084/m9.figshare.c.978904.v5
https://doi.org/10.1038/sdata.2014.22

Download Dataset XYZ file Description: 10,000 configurations of organosilicon compounds with energies predicted by an improved GFN-xTB Hamiltonian parameterization, using revPBE.

ColabFit ID: GFN-xTB_JCIM_2021_KomissarovVerstraelen__DS_0x8ozlt9g3y5_0
Name: GFN-xTB_JCIM_2021
Authors: Leonid Komissarov, Toon Verstraelen
Elements: C, H, O, Si, N, F, Cl, S, P, Br
Number of Configurations: 157,367
Number of Elements: 10
Number of Atoms: 4,022,149
Property Types: potential-energy

Links:
https://doi.org/10.24435/materialscloud:14-4m
https://doi.org/10.1021/acs.jcim.1c01170

Download Dataset XYZ file Description: This database contains computationally generated atomic structures of glass-ceramics lithium thiophosphates (gc-LPS) with the general composition (Li2S)x(P2S5)1-x. Total energies and interatomic forces from density-functional theory (DFT) calculations are included. The DFT calculations used projector-augmented-wave (PAW) pseudopotentials and the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional as implemented in the Vienna Ab Initio Simulation Package (VASP) and a kinetic energy cutoff of 520 eV. The first Brillouin zone was sampled using VASP's fully automatic k-point scheme with a length parameter Rk = 25Å. The gc-LPS structures were generated using a combination of different sampling methods. Initial amorphous structure models were generated with ab initio molecular dynamics (AIMD) simulations of supercells at 1200 K using a Nose-Hoover thermostat with a time step of 1 fs. To obtain near-ground-state structures as reference for the machine-learning potential, 150 evenly spaced snapshots were extracted from the AIMD trajectories that were reoptimized with DFT geometry optimizations at zero Kelvin. Additional structures were generated by scaling the lattice parameters of the crystalline LPS structures (see below) by ±15% and perturbing atomic positions in AIMD simulations as described above.The resulting database was used to train a specialized ANN potential for the sampling of structures along the Li2S-P2S5 composition line with a genetic-algorithm (GA) as implemented in the atomistic evolution (ævo) package, following a previously reported protocol. Starting from supercells of the ideal crystal structures, either Li and S atoms were removed with a ratio of 2:1, or P and S atoms were removed with a ratio of 2:5, and low-energy configurations were determined with GA sampling. A population size of 32 trials and a mutation rate of 10% were employed. The ANN potential was iteratively refined by including additional sampled structures in the training. For each composition, at least 10 lowest energy structure models identified with the ANN-GA approach were selected and fully relaxed with DFT.Also included in the present database are the XSF files of the previously reported crystalline phases LiPS3, Li2PS3, Li4P2S7, Li7P3S11, α-Li3PS4, β-Li3PS4, γ-Li3PS4, and Li48P16S61. The crystal structures were obtained from the Inorganic Crystal Structure Database (ICSD). the Materials Project (MP) database, the Open Quantum Materials Database (OQMD), and the AFLOW database. The configuration names indicate the journal reference and the database.

ColabFit ID: glass-ceramic_lithium_thiophosphate_electrolytes__GuoArtrith__DS_cpznjcu51bvg_0
Name: glass-ceramic_lithium_thiophosphate_electrolytes_
Authors: Haoyue Guo, Nongnuch Artrith
Elements: Li, P, S
Number of Configurations: 6,055
Number of Elements: 3
Number of Atoms: 264,604
Property Types: atomic-forces, potential-energy

Links:
https://doi.org/10.24435/materialscloud:j5-tz
https://doi.org/10.1021/acs.chemmater.2c00267

Download Dataset XYZ file Description: The extended training dataset for GST_GAP_22, calculated using the PBEsol functional. New configurations, simulated under external electric fields, were labelled with DFT and added to the original reference database GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.

ColabFit ID: GST_GAP_22_extended_ZhouZhangMaDeringer__DS_efcq7a0h6z5w_0
Name: GST_GAP_22_extended
Authors: Yuxing Zhou, Wei Zhang, Evan Ma, Volker L. Deringer
Elements: Ge, Sb, Te
Number of Configurations: 2,916
Number of Elements: 3
Number of Atoms: 399,247
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.5281/zenodo.8208202
https://doi.org/10.1038/s41928-023-01030-x

Download Dataset XYZ file Description: The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.

ColabFit ID: GST_GAP_22_main_ZhouZhangMaDeringer__DS_r3hav37ufnmb_0
Name: GST_GAP_22_main
Authors: Yuxing Zhou, Wei Zhang, Evan Ma, Volker L. Deringer
Elements: Ge, Sb, Te
Number of Configurations: 2,692
Number of Elements: 3
Number of Atoms: 341,068
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.5281/zenodo.8208202
https://doi.org/10.1038/s41928-023-01030-x

Download Dataset XYZ file Description: The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.

ColabFit ID: GST_GAP_22_refitted_ZhouZhangMaDeringer__DS_jy3ylaf48xg3_0
Name: GST_GAP_22_refitted
Authors: Yuxing Zhou, Wei Zhang, Evan Ma, Volker L. Deringer
Elements: Ge, Sb, Te
Number of Configurations: 2,692
Number of Elements: 3
Number of Atoms: 341,004
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.5281/zenodo.8208202
https://doi.org/10.1038/s41928-023-01030-x

Download Dataset XYZ file Description: Over 300,000 configurations in an expanded dataset of 19 hydrogen combustion reaction channels. Intrinsic reaction coordinate calculations (IRC) are combined with ab initio simulations (AIMD) and normal mode displacement (NM) calculations.

ColabFit ID: H_nature_2022_GuanDasSteinHeidar-ZadehBertelsLiuHaghighatlariLiZhangHaoLevenHead-GordonHead-Gordon__DS_lvixye0ynk1o_0
Name: H_nature_2022
Authors: Xingyi Guan, Akshaya Das, Christopher J. Stein, Farnaz Heidar-Zadeh, Luke Bertels, Meili Liu, Mojtaba Haghighatlari, Jie Li, Oufan Zhang, Hongxia Hao, Itai Leven, Martin Head-Gordon, Teresa Head-Gordon
Elements: H, O
Number of Configurations: 350,121
Number of Elements: 2
Number of Atoms: 1,513,654
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.6084/m9.figshare.19601689.v3
https://doi.org/10.1038/s41597-022-01330-5

Download Dataset XYZ file Description: Approximately 28,000 configurations split into 4 datasets, each using a different functional, used in the training of a high-dimensional neural network potential (HDNNP).

ColabFit ID: HDNNP_H2O_MorawietzBehler__DS_in2572v33lon_0
Name: HDNNP_H2O
Authors: Tobias Morawietz, Jörg Behler
Elements: H, O
Number of Configurations: 28,678
Number of Elements: 2
Number of Atoms: 2,327,628
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.5281/zenodo.2634097
https://doi.org/10.1073/pnas.1602375113
https://doi.org/10.1103/PhysRevLett.98.146401

Download Dataset XYZ file Description: Dataset from "Modeling high-entropy transition-metal alloys with alchemical compression". Includes 25,000 structures utilized for fitting the aforementioned potential, with a focus on 25 d-block transition metals, excluding Tc, Cd, Re, Os and Hg. Each configuration includes a "class" field, indicating the crystal class of the structure. The class represents the following: 1: perfect crystals; 3-8 elements per structure, 2: shuffled positions (standard deviation 0.2\AA ); 3-8 elements per structure, 3: shuffled positions (standard deviation 0.5\AA ); 3-8 elements per structure, 4: shuffled positions (standard deviation 0.2\AA ); 3-25 elements per structure. Configuration sets include divisions into fcc and bcc crystals, further split by class as described above.

ColabFit ID: HEA25_high_entropy_transition-metal_alloys_LopanitsynaFrauxSpringerDeCeriotti__DS_hvx1spm3gmia_0
Name: HEA25_high_entropy_transition-metal_alloys
Authors: Nataliya Lopanitsyna, Guillaume Fraux, Maximilian A. Springer, Sandip De, Michele Ceriotti
Elements: Au, Fe, Ir, Mo, Sc, V, Y, Lu, Nb, Ru, Ti, Zn, Ag, Co, Cr, Hf, Ni, Ta, Pd, Rh, W, Pt, Cu, Zr, Mn
Number of Configurations: 25,627
Number of Elements: 25
Number of Atoms: 1,063,680
Property Types: potential-energy, free-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:73-yn
http://doi.org/10.48550/arXiv.2212.13254

Download Dataset XYZ file Description: Dataset from "Surface segregation in high-entropy alloys from alchemical machine learning: dataset HEA25S". Includes 10000 bulk HEA structures (Dataset O), 2640 HEA surface slabs (Dataset A), together with 1000 bulk and 1000 surface slabs snapshots from the molecular dynamics (MD) runs (Datasets B and C), and 500 MD snapshots of the 25 elements Cantor-style alloy surface slabs. These splits, along with their respective train, test, and validation splits, are included as configuration sets.

ColabFit ID: HEA25S_high_entropy_alloys_MazitovSpringerLopanitsynaFrauxDeCeriotti__DS_d5zht3ykr8hi_0
Name: HEA25S_high_entropy_alloys
Authors: Arslan Mazitov, Maximilian A. Springer, Nataliya Lopanitsyna, Guillaume Fraux, Sandip De, Michele Ceriotti
Elements: Au, Fe, Ir, Mo, Sc, V, Y, Ag, Co, Cr, Hf, Ni, Ta, Ti, Pd, Rh, Pt, Nb, Mn, Cu, Lu, Zn, Zr, Ru, W
Number of Configurations: 15,004
Number of Elements: 25
Number of Atoms: 633,387
Property Types: potential-energy, free-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:ps-20
http://doi.org/10.48550/arXiv.2310.07604

Download Dataset XYZ file Description: Approximately 28,500 configurations of hafnia (HfO2) used in the training of a DP model for the prediction of properties of various hafnia polymorphs, including transition barriers between different phases.

ColabFit ID: HfO2_DPGEN_PRB_2021_WuZhangZhangLiu__DS_qgi1kbtdzwmm_0
Name: HfO2_DPGEN_PRB_2021
Authors: Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu
Elements: Hf, O
Number of Configurations: 28,564
Number of Elements: 2
Number of Atoms: 2,741,376
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://www.aissquare.com/datasets/detail?pageType=datasets&name=HfO2-dpgen
https://doi.org/10.1103/PhysRevB.103.024108

Download Dataset XYZ file Description: 6000 configurations of liquid and amorphous HfO2 generated for use with an active learning ML model.

ColabFit ID: HfO2_NPJ_2020_SivaramanKrishnamoorthyBaurHolmStanCsanyiBenmoreVazquez-Mayagoitia__DS_u40eq96ge40x_0
Name: HfO2_NPJ_2020
Authors: Ganesh Sivaraman, Anand Narayanan Krishnamoorthy, Matthias Baur, Christian Holm, Marius Stan, Gábor Csányi, Chris Benmore, Álvaro Vázquez-Mayagoitia
Elements: Hf, O
Number of Configurations: 6,000
Number of Elements: 2
Number of Atoms: 576,000
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/argonne-lcf/active-learning-md
https://doi.org/10.1038/s41524-020-00367-7

Download Dataset XYZ file Description: The test set from HME21. The high-temperature multi-element 2021 (HME21) dataset comprises approximately 25,000 configurations, including 37 elements, used in the training of a universal NNP called PreFerential Potential (PFP). The dataset specifically contains disordered and unstable structures, and structures that include irregular substitutions, as well as varied temperature and density.

ColabFit ID: HME21_test_TakamotoShinagawaMotokiNakagoLiKurataWatanabeYayamaIriguchiAsanoOnoderaIshiiKudoOnoSawadaIshitaniOngYamaguchiKataokaHayashiCharoenphakdeeIbuka__DS_cpgyq72fs7uk_0
Name: HME21_test
Authors: So Takamoto, Chikashi Shinagawa, Daisuke Motoki, Kosuke Nakago, Wenwen Li, Iori Kurata, Taku Watanabe, Yoshihiro Yayama, Hiroki Iriguchi, Yusuke Asano, Tasuku Onodera, Takafumi Ishii, Takao Kudo, Hideki Ono, Ryohto Sawada, Ryuichiro Ishitani, Marc Ong, Taiki Yamaguchi, Toshiki Kataoka, Akihide Hayashi, Nontawat Charoenphakdee, Takeshi Ibuka
Elements: Ba, Ca, Co, Cr, In, Ir, Mo, Na, Ni, Pb, Rh, Sc, Ti, V, Ag, Cu, F, Mg, Mn, N, Pt, Ru, S, Sn, C, O, Al, Au, H, K, Pd, Si, Zn, Fe, P, Cl, Li
Number of Configurations: 2,495
Number of Elements: 37
Number of Atoms: 69,572
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.6084/m9.figshare.19658538.v2
https://doi.org/10.1038/s41467-022-30687-9

Download Dataset XYZ file Description: The training set from HME21. The high-temperature multi-element 2021 (HME21) dataset comprises approximately 25,000 configurations, including 37 elements, used in the training of a universal NNP called PreFerential Potential (PFP). The dataset specifically contains disordered and unstable structures, and structures that include irregular substitutions, as well as varied temperature and density.

ColabFit ID: HME21_train_TakamotoShinagawaMotokiNakagoLiKurataWatanabeYayamaIriguchiAsanoOnoderaIshiiKudoOnoSawadaIshitaniOngYamaguchiKataokaHayashiCharoenphakdeeIbuka__DS_jhfis7syauhm_0
Name: HME21_train
Authors: So Takamoto, Chikashi Shinagawa, Daisuke Motoki, Kosuke Nakago, Wenwen Li, Iori Kurata, Taku Watanabe, Yoshihiro Yayama, Hiroki Iriguchi, Yusuke Asano, Tasuku Onodera, Takafumi Ishii, Takao Kudo, Hideki Ono, Ryohto Sawada, Ryuichiro Ishitani, Marc Ong, Taiki Yamaguchi, Toshiki Kataoka, Akihide Hayashi, Nontawat Charoenphakdee, Takeshi Ibuka
Elements: Pb, Ba, Co, Cr, Cu, Mg, Mn, Mo, Ni, O, S, Sc, Si, V, Zn, Fe, Ir, Li, P, Pt, Ru, Sn, Ca, F, Au, Cl, H, Ag, C, In, K, N, Pd, Rh, Na, Ti, Al
Number of Configurations: 19,956
Number of Elements: 37
Number of Atoms: 555,050
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.6084/m9.figshare.19658538.v2
https://doi.org/10.1038/s41467-022-30687-9

Download Dataset XYZ file Description: The validation set from HME21. The high-temperature multi-element 2021 (HME21) dataset comprises approximately 25,000 configurations, including 37 elements, used in the training of a universal NNP called PreFerential Potential (PFP). The dataset specifically contains disordered and unstable structures, and structures that include irregular substitutions, as well as varied temperature and density.

ColabFit ID: HME21_validation_TakamotoShinagawaMotokiNakagoLiKurataWatanabeYayamaIriguchiAsanoOnoderaIshiiKudoOnoSawadaIshitaniOngYamaguchiKataokaHayashiCharoenphakdeeIbuka__DS_vf8hkwxhibio_0
Name: HME21_validation
Authors: So Takamoto, Chikashi Shinagawa, Daisuke Motoki, Kosuke Nakago, Wenwen Li, Iori Kurata, Taku Watanabe, Yoshihiro Yayama, Hiroki Iriguchi, Yusuke Asano, Tasuku Onodera, Takafumi Ishii, Takao Kudo, Hideki Ono, Ryohto Sawada, Ryuichiro Ishitani, Marc Ong, Taiki Yamaguchi, Toshiki Kataoka, Akihide Hayashi, Nontawat Charoenphakdee, Takeshi Ibuka
Elements: Ca, Cl, Cu, Ir, K, Li, Mg, O, Pt, S, Zn, Pb, Pd, Cr, H, Ti, Ba, Co, Fe, In, Mn, N, Na, P, Rh, V, Ag, Al, Au, Mo, Si, Ru, Sc, Sn, F, C, Ni
Number of Configurations: 2,498
Number of Elements: 37
Number of Atoms: 69,420
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.6084/m9.figshare.19658538.v2
https://doi.org/10.1038/s41467-022-30687-9

Download Dataset XYZ file Description: Training and testing configurations of bulk water from HO_LiMoNiTi_NPJCM_2020 used in the training of an ANN, whereby total energy is extrapolated by a Taylor expansion as a means of reducing computational costs.

ColabFit ID: HO_LiMoNiTi_NPJCM_2020_bulk_water_train_test_CooperKastnerUrbanArtrith__DS_jjiywlyfpnme_0
Name: HO_LiMoNiTi_NPJCM_2020_bulk_water_train_test
Authors: April M. Cooper, Johannes Kästner, Alexander Urban, Nongnuch Artrith
Elements: H, O
Number of Configurations: 700
Number of Elements: 2
Number of Atoms: 134,400
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:2020.0037/v1
https://doi.org/10.1038/s41524-020-0323-8

Download Dataset XYZ file Description: Validation configurations of bulk water from HO_LiMoNiTi_NPJCM_2020 used in the training of an ANN, whereby total energy is extrapolated by a Taylor expansion as a means of reducing computational costs.

ColabFit ID: HO_LiMoNiTi_NPJCM_2020_bulk_water_validation_CooperKastnerUrbanArtrith__DS_w3whk1nr1bld_0
Name: HO_LiMoNiTi_NPJCM_2020_bulk_water_validation
Authors: April M. Cooper, Johannes Kästner, Alexander Urban, Nongnuch Artrith
Elements: H, O
Number of Configurations: 2,112
Number of Elements: 2
Number of Atoms: 405,504
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:2020.0037/v1
https://doi.org/10.1038/s41524-020-0323-8

Download Dataset XYZ file Description: Training configurations of Li8Mo2Ni7Ti7O32 from HO_LiMoNiTi_NPJCM_2020 used in the training of an ANN, whereby total energy is extrapolated by a Taylor expansion as a means of reducing computational costs.

ColabFit ID: HO_LiMoNiTi_NPJCM_2020_LiMoNiTi_train_CooperKastnerUrbanArtrith__DS_1i4q8uyams32_0
Name: HO_LiMoNiTi_NPJCM_2020_LiMoNiTi_train
Authors: April M. Cooper, Johannes Kästner, Alexander Urban, Nongnuch Artrith
Elements: Li, Mo, Ni, O, Ti
Number of Configurations: 824
Number of Elements: 5
Number of Atoms: 46,144
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:2020.0037/v1
https://doi.org/10.1038/s41524-020-0323-8

Download Dataset XYZ file Description: Validation configurations of Li8Mo2Ni7Ti7O32 from HO_LiMoNiTi_NPJCM_2020 used in the training of an ANN, whereby total energy is extrapolated by a Taylor expansion as a means of reducing computational costs.

ColabFit ID: HO_LiMoNiTi_NPJCM_2020_LiMoNiTi_validation_CooperKastnerUrbanArtrith__DS_fgjil336jos6_0
Name: HO_LiMoNiTi_NPJCM_2020_LiMoNiTi_validation
Authors: April M. Cooper, Johannes Kästner, Alexander Urban, Nongnuch Artrith
Elements: Li, Mo, Ni, O, Ti
Number of Configurations: 1,792
Number of Elements: 5
Number of Atoms: 100,352
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:2020.0037/v1
https://doi.org/10.1038/s41524-020-0323-8

Download Dataset XYZ file Description: Configurations of water clusters from HO_LiMoNiTi_NPJCM_2020 used in the training of an ANN, whereby total energy is extrapolated by a Taylor expansion as a means of reducing computational costs.

ColabFit ID: HO_LiMoNiTi_NPJCM_2020_water_clusters_CooperKastnerUrbanArtrith__DS_or3nu4t64mvk_0
Name: HO_LiMoNiTi_NPJCM_2020_water_clusters
Authors: April M. Cooper, Johannes Kästner, Alexander Urban, Nongnuch Artrith
Elements: H, O
Number of Configurations: 1,848
Number of Elements: 2
Number of Atoms: 33,264
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:2020.0037/v1
https://doi.org/10.1038/s41524-020-0323-8

Download Dataset XYZ file Description: 1590 configurations of H2O/water with total energy and forces calculated using a hybrid approach at DFT/revPBE0-D3 level of theory.

ColabFit ID: HO_PNAS_2019_ChengEngelBehlerDellagoCeriotti__DS_o73heom14iew_0
Name: HO_PNAS_2019
Authors: Bingqing Cheng, Edgar A. Engel, Jörg Behler, Christoph Dellago, Michele Ceriotti
Elements: H, O
Number of Configurations: 1,588
Number of Elements: 2
Number of Atoms: 304,896
Property Types: potential-energy, atomic-forces

Links:
https://archive.materialscloud.org/record/2018.0020/v1
https://doi.org/10.1073/pnas.1815117116

Download Dataset XYZ file Description: A training dataset of 90,000 configurations with interaction properties between H2 and Pt(111) surfaces.

ColabFit ID: HPt_NC_2022_VandermauseXieLimOwenKozinsky__DS_fhvr3y1alb97_0
Name: HPt_NC_2022
Authors: Jonathan Vandermause, Yu Xie, Jin Soo Lim, Cameron J. Owen, Boris Kozinsky
Elements: H, Pt
Number of Configurations: 90,740
Number of Elements: 2
Number of Atoms: 5,706,023
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:r0-84
https://doi.org/10.1038/s41467-022-32294-0

Download Dataset XYZ file Description: A dataset of DFT-calculated energies created to investigate the effect of hydrogen doping on the crystal structure and the electronic state in SmNiO3.Configuration sets include sets for apically and side-bonded hydrogen atoms for 1-9 hydrogen atoms.

ColabFit ID: Hydrogen-induced_insulating_state_SmNiO3_YamauchiHamada__DS_r1sl5dye35kg_0
Name: Hydrogen-induced_insulating_state_SmNiO3
Authors: Kunihiko Yamauchi, Ikutaro Hamada
Elements: H, Ni, O, Sm
Number of Configurations: 3,318
Number of Elements: 4
Number of Atoms: 156,419
Property Types: potential-energy

Links:
https://doi.org/10.24435/materialscloud:4w-qm
https://doi.org/10.48550/arXiv.2210.07656

Download Dataset XYZ file Description: Approximately 11,500 configurations of In2Se3, including monolayer (20-atom slab) and bulk (30-atom supercell) models.

ColabFit ID: In2Se3_2D_DPGEN_WuBaiHuangMaLiuLiu__DS_v4e6t79tt5xh_0
Name: In2Se3_2D_DPGEN
Authors: Jing Wu, Liyi Bai, Jiawei Huang, Liyang Ma, Jian Liu, Shi Liu
Elements: In, Se
Number of Configurations: 11,523
Number of Elements: 2
Number of Atoms: 248,510
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://www.aissquare.com/datasets/detail?pageType=datasets&name=In2Se3-2D-dpgen
https://doi.org/10.1103/PhysRevB.104.174107

Download Dataset XYZ file Description: This data set was used to generate a multi-element linear SNAP potential for InP, as published in Cusentino, M. A. et. al, J. Chem. Phys. (2020). Intended to produce an interatomic potential for indium phosphide capable of capturing high-energy defects that result from radiation damage cascades.

ColabFit ID: InP_JPCA2020_CusentinoWoodThompson__DS_03ph25s4yepy_0
Name: InP_JPCA2020
Authors: Mary Alice Cusentino, Mitchell A. Wood, Aidan P. Thompson
Elements: In, P
Number of Configurations: 1,802
Number of Elements: 2
Number of Atoms: 106,761
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/FitSNAP/FitSNAP/tree/master/examples/InP_JPCA2020
https://doi.org/10.1021/acs.jpca.0c02450

Download Dataset XYZ file Description: 129 molecules of composition C7O2H10 from the QM9 dataset with 5000 conformational geometries apiece. Molecular dynamics data was simulated using the Fritz-Haber Institute ab initio simulation software.

ColabFit ID: ISO17_NC_2017_VandermauseXieLimOwenKozinsky__DS_ngsaypfb9rnj_0
Name: ISO17_NC_2017
Authors: Jonathan Vandermause, Yu Xie, Jin Soo Lim, Cameron J. Owen, Boris Kozinsky
Elements: C, H, O
Number of Configurations: 640,855
Number of Elements: 3
Number of Atoms: 12,176,245
Property Types: potential-energy, atomic-forces

Links:
http://quantum-machine.org/datasets/
https://proceedings.neurips.cc/paper/2017/hash/303ed4c69846ab36c2904d3ba8573050-Abstract.html
https://doi.org/10.1038/s41467-019-12875-2
https://doi.org/10.1038/ncomms13890
https://doi.org/10.1038/sdata.2014.22

Download Dataset XYZ file Description: The JARVIS-2DMatPedia dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This subset contains configurations with 2D materials from the 2DMatPedia database, generated through two methods: a top-down exfoliation approach, using structures of bulk materials from the Materials Project database; and a bottom-up approach, replacing each element in a 2D material with another from the same group (according to column number). JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS-2DMatPedia_ZhouShenCostaPerssonHuckLuMaChenTangFeng__DS_hdv6si8yu2mv_0
Name: JARVIS_2DMatPedia
Authors: Jun Zhou, Lei Shen, Miguel Dias Costa, Kristin A. Persson, Shyue Ping Ong", "Patrick Huck, Yunhao Lu, Xiaoyang Ma, Yiming Chen, Hanmei Tang, Yuan Ping Feng
Elements: Cl, Tl, Mg, Na, Sb, Br, Mn, S, Cd, Al, Se, I, Mo, Re, F, Zr, P, Be, O, W, In, Te, Ru, B, Ba, Ga, Ho, K, V, Zn, Sr, Ta, Hf, Pd, N, Tc, Sn, Ti, Pu, Pt, Os, Y, Bi, Ca, Rh, As, Fe, Cr, C, Nb, Co, Li, Tb, Dy, Ce, Pb, Er, Yb, Si, Ir, Cu, Ge, H, Rb, Au, Eu, Sc, Ni, Hg, Ag, Np, Pr, Lu, Sm, La, U, Tm, Th, Nd, Pa, Xe, Gd, Ac
Number of Configurations: 6,351
Number of Elements: 83
Number of Atoms: 66,295
Property Types: potential-energy, band-gap

Links:
https://ndownloader.figshare.com/files/26789006
https://doi.org/10.1038/s41597-019-0097-3
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The JARVIS_AGRA_CHO dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This dataset contains data from the CO2 reduction reaction (CO2RR) dataset from Chen et al., as used in the automated graph representation algorithm (AGRA) training dataset: a collection of DFT training data for training a graph representation method to extract the local chemical environment of metallic surface adsorption sites. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_AGRA_CHO_ChenGariepyChenYaoAnandLiuFeugmoTamblynSingh__DS_z3s0qui5vg5c_0
Name: JARVIS_AGRA_CHO
Authors: Zhi Wen Chen, Zachary Gariepy, Lixin Chen, Xue Yao, Abu Anand, Szu-Jia Liu, Conrard Giresse Tetsassi Feugmo, Isaac Tamblyn, Chandra Veer Singh
Elements: C, Co, Cu, Fe, H, Mo, Ni, O
Number of Configurations: 216
Number of Elements: 8
Number of Atoms: 14,472
Property Types: adsorption-energy

Links:
https://figshare.com/ndownloader/files/41923284
https://doi.org/10.1021/acscatal.2c03675
https://github.com/Feugmo-Group/AGRA
https://jarvis.nist.gov/
https://doi.org/10.1063/5.0140487

Download Dataset XYZ file Description: The JARVIS_AGRA_CO dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This dataset contains data from the CO2 reduction reaction (CO2RR) dataset from Chen et al., as used in the automated graph representation algorithm (AGRA) training dataset: a collection of DFT training data for training a graph representation method to extract the local chemical environment of metallic surface adsorption sites. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_AGRA_CO_ChenGariepyChenYaoAnandLiuFeugmoTamblynSingh__DS_1ynp8v7d3er5_0
Name: JARVIS_AGRA_CO
Authors: Zhi Wen Chen, Zachary Gariepy, Lixin Chen, Xue Yao, Abu Anand, Szu-Jia Liu, Conrard Giresse Tetsassi Feugmo, Isaac Tamblyn, Chandra Veer Singh
Elements: C, Co, Cu, Fe, Mo, Ni, O
Number of Configurations: 194
Number of Elements: 7
Number of Atoms: 12,804
Property Types: adsorption-energy

Links:
https://figshare.com/ndownloader/files/41923284
https://doi.org/10.1021/acscatal.2c03675
https://github.com/Feugmo-Group/AGRA
https://jarvis.nist.gov/
https://doi.org/10.1063/5.0140487

Download Dataset XYZ file Description: The JARVIS_AGRA_COOH dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This dataset contains data from the CO2 reduction reaction (CO2RR) dataset from Chen et al., as used in the automated graph representation algorithm (AGRA) training dataset: a collection of DFT training data for training a graph representation method to extract the local chemical environment of metallic surface adsorption sites. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_AGRA_COOH_ChenGariepyChenYaoAnandLiuFeugmoTamblynSingh__DS_bggn2p6qw8zv_0
Name: JARVIS_AGRA_COOH
Authors: Zhi Wen Chen, Zachary Gariepy, Lixin Chen, Xue Yao, Abu Anand, Szu-Jia Liu, Conrard Giresse Tetsassi Feugmo, Isaac Tamblyn, Chandra Veer Singh
Elements: C, Co, Cu, Fe, H, Mo, Ni, O
Number of Configurations: 280
Number of Elements: 8
Number of Atoms: 19,040
Property Types: adsorption-energy

Links:
https://figshare.com/ndownloader/files/41923284
https://doi.org/10.1021/acscatal.2c03675
https://github.com/Feugmo-Group/AGRA
https://jarvis.nist.gov/
https://doi.org/10.1063/5.0140487

Download Dataset XYZ file Description: The JARVIS_AGRA_O dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This dataset contains data from the training set for the oxygen reduction reaction (ORR) dataset from Batchelor et al., as used in the automated graph representation algorithm (AGRA) training dataset: a collection of DFT training data for training a graph representation method to extract the local chemical environment of metallic surface adsorption sites. Bulk calculations were performed with k-point = 8 x 8 x 4. Training adsorption energies were calculated on slabs, k-point = 4 x 4 x 1, while testing energies used k-point = 3 x 3 x 1. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_AGRA_O_BatchelorPedersenWintherCastelliJacobsenRossmeisl__DS_ikx8oaixaoz0_0
Name: JARVIS_AGRA_O
Authors: Thomas A.A. Batchelor, Jack K. Pedersen, Simon H. Winther, Ivano E. Castelli, Karsten W. Jacobsen, Jan Rossmeisl
Elements: Ir, O, Pd, Pt, Rh, Ru
Number of Configurations: 1,000
Number of Elements: 6
Number of Atoms: 17,000
Property Types: adsorption-energy, potential-energy

Links:
https://figshare.com/ndownloader/files/41923284
https://doi.org/10.1016/j.joule.2018.12.015
https://github.com/Feugmo-Group/AGRA
https://jarvis.nist.gov/
https://doi.org/10.1063/5.0140487

Download Dataset XYZ file Description: The JARVIS_AGRA_OH dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This dataset contains data from the training set for the oxygen reduction reaction (ORR) dataset from Batchelor et al., as used in the automated graph representation algorithm (AGRA) training dataset: a collection of DFT training data for training a graph representation method to extract the local chemical environment of metallic surface adsorption sites. Bulk calculations were performed with k-point = 8 x 8 x 4. Training adsorption energies were calculated on slabs, k-point = 4 x 4 x 1, while testing energies used k-point = 3 x 3 x 1. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_AGRA_OH_BatchelorPedersenWintherCastelliJacobsenRossmeisl__DS_osavzsrz5mgf_0
Name: JARVIS_AGRA_OH
Authors: Thomas A.A. Batchelor, Jack K. Pedersen, Simon H. Winther, Ivano E. Castelli, Karsten W. Jacobsen, Jan Rossmeisl
Elements: H, O, Pd, Pt, Rh, Ru, Ir
Number of Configurations: 877
Number of Elements: 7
Number of Atoms: 15,786
Property Types: adsorption-energy, potential-energy

Links:
https://figshare.com/ndownloader/files/41923284
https://doi.org/10.1016/j.joule.2018.12.015
https://github.com/Feugmo-Group/AGRA
https://jarvis.nist.gov/
https://doi.org/10.1063/5.0140487

Download Dataset XYZ file Description: The JARVIS_ALIGNN_FF dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset is a subset of the JARVIS DFT dataset, filtered to contain just the first, last, middle, maximum energy and minimum energy structures. Additionally, calculation run snapshots are filtered for uniqueness, and the dataset contains only perfect structures. DFT energies, stresses and forces in this dataset were used to train an atomisitic line graph neural network (ALIGNN)-based FF model. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_ALIGNN_FF_ChoudharyDeCostMajorButlerThiyagalingamTavazza__DS_720rbshv96l1_0
Name: JARVIS_ALIGNN_FF
Authors: Kamal Choudhary, Brian DeCost, Lily Major, Keith Butler, Jeyan Thiyagalingam, Francesca Tavazza
Elements: F, O, V, Ba, Mg, Te, Ag, Sc, Au, K, Sb, C, Cd, Li, Mn, P, Cu, In, Pm, Nd, Rh, Hg, Fe, Si, Tb, Be, Se, Na, S, As, Al, Ga, Yb, Pt, Ca, Ge, H, Co, Ta, Hf, Sn, Br, Sm, Cl, Pb, Mo, B, N, Zr, Ni, Tl, Sr, W, I, Os, Tc, Y, Dy, Rb, Ho, Pd, Ti, Pr, Tm, Bi, Ac, Nb, Ir, Ce, Zn, Cr, Np, Ru, La, Lu, Re, Th, U, Er, Xe, Pu, Pa, Ar, He, Cs, Eu, Ne, Kr, Gd
Number of Configurations: 306,411
Number of Elements: 89
Number of Atoms: 3,193,703
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://ndownloader.figshare.com/files/38522315
https://doi.org/10.1039/D2DD00096B
https://github.com/usnistgov/alignn
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The JARVIS-C2DB dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This subset contains configurations from the Computational 2D Database (C2DB), which contains a variety of properties for 2-dimensional materials across more than 30 differentcrystal structures. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS-C2DB_HaastrupStrangePandeyDeilmannSchmidtHinscheGjerdingTorelliLarsenRiis-JensenGathJacobsenMortensenOlsenThygesen__DS_8hgxhsfkcfa7_0
Name: JARVIS_C2DB
Authors: Sten Haastrup, Mikkel Strange, Mohnish Pandey, Thorsten Deilmann, Per S Schmidt, Nicki F Hinsche, Morten N Gjerding, Daniele Torelli, Peter M Larsen, Anders C Riis-Jensen, Jakob Gath, Karsten W Jacobsen, Jens Jørgen Mortensen, Thomas Olsen, Kristian S Thygesen
Elements: Te, Ti, Ag, Ca, I, S, C, H, O, W, In, Br, Cr, N, F, Mg, Al, Pd, Ni, Se, Zr, Co, Cl, Cs, P, Hf, Zn, Sr, Y, V, As, Ir, Rh, Ta, Bi, Mn, Mo, Pt, Nb, Sb, Hg, Ge, Sc, Cu, B, Sn, Au, Pb, Ru, Si, Fe, Ga, Re, Tl, Os, K, Li, Rb, Cd, Ba, Na
Number of Configurations: 3,520
Number of Elements: 61
Number of Atoms: 17,990
Property Types: potential-energy, band-gap

Links:
https://ndownloader.figshare.com/files/28682010
https://doi.org/10.1088/2053-1583/aacfc1
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The JARVIS_CFID_3D_8_18_2022 dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset contains configurations of 3D materials. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_CFID_3D_8_18_2022_ChoudharyGarrityReidDeCostBiacchiWalkerTrauttHattrick-SimpersKusneCentroneDavydovJiangPachterCheonReedAgrawalQianSharmaZhuangKalininSumpterPilaniaAcarMandalHauleVanderbiltRabeTavazza__DS_np1noc8iip47_0
Name: JARVIS_CFID_3D_8_18_2022
Authors: Kamal Choudhary, Kevin F. Garrity, Andrew C. E. Reid, Brian DeCost, Adam J. Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, A. Gilad Kusne, Andrea Centrone, Albert Davydov, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei V. Kalinin, Bobby G. Sumpter, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, Kristjan Haule, David Vanderbilt, Karin Rabe, Francesca Tavazza
Elements: Cd, F, Al, N, Zr, Mn, O, P, V, Bi, Ca, Si, Fe, Li, Ba, Hf, S, B, Be, Sc, Sr, Ni, Ac, Ag, In, Te, Pb, Pd, K, Na, Sb, Pt, H, Ho, Rh, Ga, Sm, Tl, Y, Nd, C, Cu, Ge, Np, Lu, Mg, Br, Se, Cr, Zn, Ti, W, Co, Sn, Tm, Cl, As, Rb, Er, Re, Nb, Ru, Au, Hg, Mo, I, Ta, Tb, Th, Tc, Ce, Dy, Ir, La, Os, Pr, Pm, U, Yb, Pu, Pa, Eu, Xe, He, Cs, Ar, Kr, Gd, Ne
Number of Configurations: 55,631
Number of Elements: 89
Number of Atoms: 561,738
Property Types: formation-energy, band-gap, potential-energy

Links:
https://doi.org/10.6084/m9.figshare.6815699
https://doi.org/10.1038/s41524-020-00440-1
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The JARVIS_CFID_OQMD dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the Open Quantum Materials Database (OQMD), created to hold information about the electronic structure and stability of organic materials for the purpose of aiding in materials discovery. Calculations were performed at the DFT level of theory, using the PAW-PBE functional implemented by VASP. This dataset also includes classical force-field inspired descriptors (CFID) for each configuration. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_CFID_OQMD_KirklinSaalMeredigThompsonDoakAykolRuhlWolverton__DS_u8strp7hm0cy_0
Name: JARVIS_CFID_OQMD
Authors: Scott Kirklin, James E Saal, Bryce Meredig, Alex Thompson, Jeff W Doak, Muratahan Aykol, Stephan Rühl, Chris Wolverton
Elements: Ce, Mg, W, Cs, Gd, Li, Ag, Ga, Si, Ho, Ru, Sc, Al, Ir, Hf, Pd, Yb, Hg, O, Zr, Ge, Te, Y, La, Mn, Pm, U, Cl, Au, Np, Ti, Re, Be, H, Pu, Sn, Tl, Na, Ni, K, Pb, Sb, As, Cu, Rb, Sr, Cd, Nb, Tm, Ac, In, Er, Fe, Cr, Lu, B, Os, Pa, Sm, Pr, Ba, Tb, Tc, Nd, Pt, Mo, C, I, N, Eu, Ca, Ta, Th, V, Zn, Rh, S, Bi, Co, P, F, Dy, Se, Br, Xe, Ne, He, Kr, Ar
Number of Configurations: 459,991
Number of Elements: 89
Number of Atoms: 2,366,255
Property Types: formation-energy

Links:
https://ndownloader.figshare.com/files/24981170
https://doi.org/10.1038/npjcompumats.2015.10
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The DFT-2D-3-12-2021 dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset contains configurations of 2D materials. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_DFT_2D_3_12_2021_ChoudharyGarrityReidDeCostBiacchiWalkerTrauttHattrick-SimpersKusneCentroneDavydovJiangPachterCheonReedAgrawalQianSharmaZhuangKalininSumpterPilaniaAcarMandalHauleVanderbiltRabeTavazza__DS_4ml1yrigmar0_0
Name: JARVIS_DFT_2D_3_12_2021
Authors: Kamal Choudhary, Kevin F. Garrity, Andrew C. E. Reid, Brian DeCost, Adam J. Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, A. Gilad Kusne, Andrea Centrone, Albert Davydov, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei V. Kalinin, Bobby G. Sumpter, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, Kristjan Haule, David Vanderbilt, Karin Rabe, Francesca Tavazza
Elements: Br, Cd, Mn, Na, P, Ge, Se, O, Yb, Bi, Cl, Te, Lu, S, Sn, I, N, Ti, Au, K, As, Pd, Rb, In, Si, Al, Ag, Tl, Zr, Ta, Pt, Nd, Cr, Fe, Hf, Ir, Nb, Hg, Li, Ga, H, Pr, Ce, Sr, Ca, Mg, Sb, Co, Ni, Ho, F, Ba, V, B, Th, W, U, Ac, Re, Zn, Sc, Tm, Er, Pu, Os, C, Tb, Y, Dy, Rh, Be, Ru, Cu, Mo, Pb, Sm, La, Tc, Kr, Np, Pa
Number of Configurations: 1,074
Number of Elements: 81
Number of Atoms: 7,903
Property Types: formation-energy, band-gap, potential-energy

Links:
https://ndownloader.figshare.com/files/26808917
https://doi.org/10.1038/s41524-020-00440-1
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The DFT_3D_12_12_2022 dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset contains configurations of 3D materials. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_DFT_3D_12_12_2022_ChoudharyGarrityReidDeCostBiacchiWalkerTrauttHattrick-SimpersKusneCentroneDavydovJiangPachterCheonReedAgrawalQianSharmaZhuangKalininSumpterPilaniaAcarMandalHauleVanderbiltRabeTavazza__DS_t02h9eraswtv_0
Name: JARVIS_DFT_3D_12_12_2022
Authors: Kamal Choudhary, Kevin F. Garrity, Andrew C. E. Reid, Brian DeCost, Adam J. Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, A. Gilad Kusne, Andrea Centrone, Albert Davydov, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei V. Kalinin, Bobby G. Sumpter, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, Kristjan Haule, David Vanderbilt, Karin Rabe, Francesca Tavazza
Elements: As, Cl, Li, Mn, O, P, Sc, Se, Zn, Ba, Fe, Sr, Co, Ga, Pr, Bi, Cs, K, Br, Hg, Sb, Au, Cu, F, Rb, B, H, Er, Mo, Ir, U, Ag, Ho, Tl, V, W, Ca, Hf, Na, Pb, S, Zr, Ge, Ti, Si, Rh, Sn, Ni, Lu, Pd, Pt, Y, I, Al, Te, Tm, Cd, Ce, C, Nb, Sm, Tb, N, Ru, Mg, Yb, Dy, Cr, In, Eu, Be, Ta, Ac, Os, Nd, Th, Tc, La, Re, Np, Ar, Ne, Pm, Pa, Pu, Kr, Xe, He, Gd
Number of Configurations: 75,909
Number of Elements: 89
Number of Atoms: 785,250
Property Types: formation-energy, band-gap, potential-energy

Links:
https://doi.org/10.6084/m9.figshare.6815699
https://doi.org/10.1038/s41524-020-00440-1
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The JARVIS_DFT_3D_8_18_2021 dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset contains configurations of 3D materials. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_DFT_3D_8_18_2022_ChoudharyGarrityReidDeCostBiacchiWalkerTrauttHattrick-SimpersKusneCentroneDavydovJiangPachterCheonReedAgrawalQianSharmaZhuangKalininSumpterPilaniaAcarMandalHauleVanderbiltRabeTavazza__DS_soziqjohq6hm_0
Name: JARVIS_DFT_3D_8_18_2021
Authors: Kamal Choudhary, Kevin F. Garrity, Andrew C. E. Reid, Brian DeCost, Adam J. Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, A. Gilad Kusne, Andrea Centrone, Albert Davydov, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei V. Kalinin, Bobby G. Sumpter, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, Kristjan Haule, David Vanderbilt, Karin Rabe, Francesca Tavazza
Elements: Li, Mn, O, P, Sc, Se, As, Zn, Ba, Fe, Sr, Co, Ga, Pr, Bi, Cs, K, Br, Hg, Sb, Au, Cu, F, Rb, B, H, Er, Mo, Ag, Ho, Tl, V, W, Hf, Na, Pb, S, Zr, Cl, Ge, Ti, Rh, Sn, Ca, Ni, Lu, Pd, Pt, Y, U, Te, Tm, Cd, Ce, C, Nb, Sm, Tb, N, Ru, Mg, Yb, I, Dy, Eu, Be, In, Al, Os, Si, Ir, Tc, Cr, Nd, Re, Th, Np, Ac, La, Pm, Ta, Pu, Pa, Ne, Xe, Ar, Kr, Gd, He
Number of Configurations: 55,631
Number of Elements: 89
Number of Atoms: 561,741
Property Types: formation-energy, band-gap, potential-energy

Links:
https://doi.org/10.6084/m9.figshare.6815699
https://doi.org/10.1038/s41524-020-00440-1
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The JARVIS_EPC_2D dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset contains configurations sourced from the JARVIS-DFT-2D dataset, rerelaxed with Quantum ESPRESSO. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_EPC_2D_WinesChoudharyBiacchiGarrityTavazza__DS_7jji22dy5hix_0
Name: JARVIS_EPC_2D
Authors: Daniel Wines, Kamal Choudhary, Adam J. Biacchi, evin F. Garrity, Francesca Tavazza
Elements: Cl, O, Ti, K, Te, Y, Ga, I, Au, Mg, B, N, S, Ni, Li, Br, Ca, Si, Ru, Ir, Ta, Fe, Se, Hf, Sn, H, C, W, Co, Nb, Sb, Al, Pd, Tl, Pt, Be, Mo, Zr, Ag, Ba, P, Sc, Sr, Bi, La, In, As, V, Cu, Ge, Na, Cr, Zn, F, Pb
Number of Configurations: 161
Number of Elements: 55
Number of Atoms: 788
Property Types: formation-energy

Links:
https://figshare.com/ndownloader/files/38950433
https://doi.org/10.1021/acs.nanolett.2c04420
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The JARVIS_HOPV dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the Harvard organic photovoltaic (HOPV) dataset, collated experimental data from the literature. Quantum-chemical calculations are performed using five functionals: NP86, PBE0, B3LYP, M06-2X and basis sets. In addition to HOMO, LUMO and gap energies, calculations include open circuit potentials (Voc), short circuit current density (Jsc) and percent conversion efficiency (PCE). JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_HOPV_LopezPyzer-KnappSimmLutzowLiSeressHachmannAspuru-Guzik__DS_mvuwxu67yrdy_0
Name: JARVIS_HOPV
Authors: Steven A. Lopez, Edward O. Pyzer-Knapp, Gregor N. Simm, Trevor Lutzow, Kewei Li, Laszlo R. Seress, Johannes Hachmann, Alán Aspuru-Guzik
Elements: C, H, N, S, O, Si, F, Se
Number of Configurations: 4,855
Number of Elements: 8
Number of Atoms: 343,972
Property Types: band-gap

Links:
https://ndownloader.figshare.com/files/28814184
https://doi.org/10.1038/sdata.2016.86
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The JARVIS_Materials_Project_2020 dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset contains 127,000 configurations of 3D materials from the Materials Project database. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_Materials_Project_2020_JainOngHautierChenRichardsDacekCholiaGunterSkinnerCederPersson__DS_gpsibs9f47k4_0
Name: JARVIS_Materials_Project_2020
Authors: Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson
Elements: Cl, Cs, Tl, Ca, O, P, Ti, F, K, Si, C, Eu, Cu, Se, Sm, Bi, Li, N, Na, Ge, V, Sb, Ir, H, Ag, Be, Br, Mo, Pr, Cr, Ba, Hf, Sr, In, Zn, Mn, Al, La, Ni, Co, Pb, Sn, Zr, Au, Lu, Mg, Tm, As, U, Ho, Y, Nb, Nd, Sc, Ce, S, Yb, Fe, Pd, I, Rb, Hg, Ga, Pt, B, Tb, Ru, Tc, Pm, Np, Te, W, Dy, Rh, Gd, Cd, Er, Pu, Ta, Os, Th, Re, Pa, Ac, Xe, Kr, He, Ar, Ne
Number of Configurations: 126,334
Number of Elements: 89
Number of Atoms: 3,725,713
Property Types: formation-energy, band-gap, potential-energy

Links:
https://ndownloader.figshare.com/files/26791259
https://doi.org/10.1063/1.4812323
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The JARVIS_Materials_Project_84K dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset contains 84,000 configurations of 3D materials from the Materials Project database. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_Materials_Project_84K_JainOngHautierChenRichardsDacekCholiaGunterSkinnerCederPersson__DS_1s8172fnm2ct_0
Name: JARVIS_Materials_Project_84K
Authors: Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson
Elements: B, Br, Cr, O, Bi, Mg, Rb, Al, Ca, Co, Mo, N, Na, Cu, Pd, Sb, I, Ba, Ge, H, Ag, Se, Fe, Sn, Li, Ti, Zn, Zr, Hg, V, Cd, Ta, K, Nd, As, Be, Dy, Ni, S, Cs, Tc, Nb, Pt, C, Cl, Pb, Mn, Ce, Rh, Te, Si, Sr, Tb, W, Hf, Os, P, F, Y, Tl, Th, Sm, U, Re, Eu, Ga, La, In, Ru, Au, Np, Ir, Pr, Tm, Er, Ho, Sc, Gd, Yb, Pm, Lu, Pa, Pu, Xe, Ac, Kr, He, Ar, Ne
Number of Configurations: 83,426
Number of Elements: 89
Number of Atoms: 2,339,932
Property Types: formation-energy

Links:
https://ndownloader.figshare.com/files/24979850
https://doi.org/10.1063/1.4812323
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The JARVIS-MEGNet dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This subset contains configurations with 3D materials properties from the 2018 version of Materials Project, as used in the training of the MEGNet ML model. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_MEGNet_ChenYeZuoZhengOng__DS_9yr94hhj1k33_0
Name: JARVIS_MEGNet
Authors: Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, Shyue Ping Ong
Elements: Al, Ce, Fe, Si, Yb, Li, O, U, Lu, Ru, Te, Mo, S, Se, W, Ba, C, H, N, P, In, Nd, Zn, Cl, Ca, Cd, Ta, Co, Ga, Hf, B, Zr, I, Rb, Mn, V, Pd, Cr, K, Tb, Bi, F, Na, Cu, La, Mg, Sn, Au, Cs, Sb, Ac, Sr, Ti, Dy, Ag, Re, Rh, Br, Ir, Sc, Ni, Er, As, Tl, Y, Sm, Eu, Pb, Ho, Nb, Pr, Tc, Gd, Ge, Hg, Pt, Th, Be, Pm, Xe, Pu, Os, Tm, Np, Pa, Kr, He, Ne, Ar
Number of Configurations: 69,234
Number of Elements: 89
Number of Atoms: 2,070,948
Property Types: formation-energy, band-gap

Links:
https://ndownloader.figshare.com/files/26724977
https://doi.org/10.1021/acs.chemmater.9b01294
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The JARVIS-MEGNet2 dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This subset contains 133K materials with formation energy from the Materials Project, as used in the training of the MEGNet ML model. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_MEGNet2_ChenYeZuoZhengOng__DS_5ar73nonq6l1_0
Name: JARVIS_MEGNet2
Authors: Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, Shyue Ping Ong
Elements: Al, Ge, Li, O, Cu, F, Ga, Na, Au, Lu, Mo, Rb, B, Dy, N, U, Mg, V, Zn, Cl, Ag, S, Fe, Sr, Br, Tl, Eu, In, Ni, As, K, Pt, Yb, Co, Mn, I, Pb, Cs, Se, H, Y, Ti, Nb, Sc, Ir, Si, C, Ca, Ho, Sn, Cd, Tm, Rh, Te, W, Sb, P, La, Xe, Bi, Cr, Zr, Nd, Ba, Ta, Pm, Er, Sm, Ce, Ru, Be, Np, Os, Hf, Re, Pd, Tb, Pr, Gd, Hg, Pa, Pu, Ac, Th, Tc, Kr, Ar, He, Ne
Number of Configurations: 133,420
Number of Elements: 89
Number of Atoms: 3,880,497
Property Types: formation-energy

Links:
https://ndownloader.figshare.com/files/28332741
https://doi.org/10.1021/acs.chemmater.9b01294
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The JARVIS_mlearn dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the Organic Materials Database (OMDB): a dataset of 12,500 crystal materials for the purpose of training models for the prediction of properties for complex and lattice-periodic organic crystals with large numbers of atoms per unit cell. Dataset covers 69 space groups, 65 elements; averages 82 atoms per unit cell. This dataset also includes classical force-field inspired descriptors (CFID) for each configuration. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_mlearn_ZuoChenLiDengChenBehlerCsanyiShapeevThompsonWoodOng__DS_g9sra4y8efji_0
Name: JARVIS_mlearn
Authors: Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, Shyue Ping Ong
Elements: Si, Cu, Li, Ni, Ge, Mo
Number of Configurations: 1,566
Number of Elements: 6
Number of Atoms: 115,742
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://figshare.com/ndownloader/files/40424156
https://doi.org/10.1021/acs.jpca.9b08723
https://jarvis.nist.gov/
https://github.com/materialsvirtuallab/mlearn

Download Dataset XYZ file Description: The JARVIS_OMDB dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the Organic Materials Database (OMDB): a dataset of 12,500 crystal materials for the purpose of training models for the prediction of properties for complex and lattice-periodic organic crystals with large numbers of atoms per unit cell. Dataset covers 69 space groups, 65 elements; averages 82 atoms per unit cell. This dataset also includes classical force-field inspired descriptors (CFID) for each configuration. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_OMDB_OlsthoornGeilhufeBorysovBalatsky__DS_hrt0twm503tr_0
Name: JARVIS_OMDB
Authors: Bart Olsthoorn, R. Matthias Geilhufe, Stanislav S. Borysov, Alexander V. Balatsky
Elements: C, H, N, O, S, Pt, P, Cl, Ru, F, B, I, Br, Mg, Pb, Si, Pd, Fe, Ge, Ga, Sn, Li, Na, Zn, W, Ag, Ti, In, Mo, Rh, Bi, Co, Hg, Se, Ni, Te, Sb, Cr, Re, K, Ca, Ir, Cu, Mn, Au, Al, Tl, Sc, Sr, As, V, Be, U, Ba, Cs, Zr, Os, Cd, Y, Lu, Ta, Hf, Nb, Rb, La
Number of Configurations: 12,497
Number of Elements: 65
Number of Atoms: 1,061,362
Property Types: band-gap

Links:
https://ndownloader.figshare.com/files/28501761
https://doi.org/10.1002/qute.201900023
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The JARVIS_Open_Catalyst_100K dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset contains configurations from the 100K training, rest validation and test dataset from the Open Catalyst Project (OCP). JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_Open_Catalyst_100K_ChanussotDasGoyalLavrilShuaibiRiviereTranHeras-DomingoHoHuPalizhatiSriramWoodYoonParikhZitnickUlissi__DS_armtbsouma25_0
Name: JARVIS_Open_Catalyst_100K
Authors: Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, Zachary Ulissi
Elements: C, Ca, H, O, Si, Sr, Pt, V, Zr, Cl, In, N, P, Pd, Hg, Na, Rh, Ni, Sn, Sc, Co, Nb, Te, Ta, Ga, Cu, Sb, Au, Y, Al, As, Mo, Mn, S, Ti, Hf, Fe, Se, Os, Re, Rb, Ru, K, Cr, Cs, Zn, Ir, W, Ag, Ge, Tl, Tc, Bi, Pb, Cd, B
Number of Configurations: 124,943
Number of Elements: 56
Number of Atoms: 9,720,870
Property Types: potential-energy

Links:
https://figshare.com/ndownloader/files/40902845
https://doi.org/10.1021/acscatal.0c04525
https://jarvis.nist.gov/
https://github.com/Open-Catalyst-Project/ocp

Download Dataset XYZ file Description: The JARVIS_Open_Catalyst_10K dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset contains configurations from the 10K training, rest validation and test dataset from the Open Catalyst Project (OCP). JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_Open_Catalyst_10K_ChanussotDasGoyalLavrilShuaibiRiviereTranHeras-DomingoHoHuPalizhatiSriramWoodYoonParikhZitnickUlissi__DS_6dcul0b17524_0
Name: JARVIS_Open_Catalyst_10K
Authors: Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, Zachary Ulissi
Elements: C, Ca, H, O, Si, Sr, Co, Ga, N, Zr, In, Ni, Sn, V, Cu, Sb, As, Mo, Ti, P, Te, Ru, Y, Fe, Pt, Rb, S, Cs, Zn, Na, Tl, Al, Ge, Os, Ta, Sc, W, Cr, Ir, Nb, Re, Tc, Hg, K, Hf, Mn, Se, Pb, Rh, Pd, Bi, Ag, Cd, Cl, Au, B
Number of Configurations: 34,943
Number of Elements: 56
Number of Atoms: 2,720,316
Property Types: potential-energy

Links:
https://figshare.com/ndownloader/files/40566122
https://doi.org/10.1021/acscatal.0c04525
https://jarvis.nist.gov/
https://github.com/Open-Catalyst-Project/ocp

Download Dataset XYZ file Description: The JARVIS_Open_Catalyst_All dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset contains configurations from the Open Catalyst Project (OCP) 460328 training, rest validation and test dataset. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_Open_Catalyst_All_ChanussotDasGoyalLavrilShuaibiRiviereTranHeras-DomingoHoHuPalizhatiSriramWoodYoonParikhZitnickUlissi__DS_3u17g7ukggi5_0
Name: JARVIS_Open_Catalyst_All
Authors: Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, Zachary Ulissi
Elements: C, H, O, Os, Re, S, V, Na, P, Tl, Ca, Mo, N, Au, Cd, Ta, Ag, Zn, In, Mn, Ni, Ir, W, Ge, Sr, Hg, Pd, Ti, Sb, Y, Zr, Al, Cu, Se, Hf, Nb, Cr, As, Co, Fe, Si, K, Ga, Pt, Sc, Sn, Ru, Cl, Cs, Pb, Tc, Te, Bi, Rb, Rh, B
Number of Configurations: 485,269
Number of Elements: 56
Number of Atoms: 37,728,919
Property Types: potential-energy

Links:
https://figshare.com/ndownloader/files/40902845
https://doi.org/10.1021/acscatal.0c04525
https://jarvis.nist.gov/
https://github.com/Open-Catalyst-Project/ocp

Download Dataset XYZ file Description: The JARVIS_OQMD_no_CFID dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the Open Quantum Materials Database (OQMD), created to hold information about the electronic structure and stability of organic materials for the purpose of aiding in materials discovery. Calculations were performed at the DFT level of theory, using the PAW-PBE functional implemented by VASP. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_OQMD_no_CFID_KirklinSaalMeredigThompsonDoakAykolRuhlWolverton__DS_untxw8nljf92_0
Name: JARVIS_OQMD_no_CFID
Authors: Scott Kirklin, James E Saal, Bryce Meredig, Alex Thompson, Jeff W Doak, Muratahan Aykol, Stephan Rühl, Chris Wolverton
Elements: Cr, Pd, Sc, Sn, Al, Nb, Zn, Cu, Os, Tm, F, In, Bi, Li, Ni, La, Sr, Th, Co, Ge, Zr, Au, N, Pt, Ti, Be, Sb, Cd, Rh, Hf, Ir, Eu, Hg, Np, Ca, K, H, S, C, O, Tb, Y, Pr, Si, Ta, Ag, W, Ho, Ru, As, U, Fe, Ga, Na, Cs, Tc, Yb, Pm, Nd, Pa, Mn, B, Rb, Sm, Pb, Mg, Gd, Tl, Cl, Ba, Lu, V, Pu, P, Br, Mo, Ce, Te, Dy, Se, Re, Er, Ac, I, Xe, Kr, Ne, Ar, He
Number of Configurations: 811,782
Number of Elements: 89
Number of Atoms: 5,017,701
Property Types: formation-energy, band-gap

Links:
https://ndownloader.figshare.com/files/26790182
https://doi.org/10.1038/npjcompumats.2015.10
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The JARVIS-Polymer-Genome dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the Polymer Genome dataset, as created for the linked publication (Huan, T., Mannodi-Kanakkithodi, A., Kim, C. et al.). Structures were curated from existing sources and the original authors' works, removing redundant, identical structures before calculations, and removing redundant datapoints after calculations were performed. Band gap energies were calculated using two different DFT functionals: rPW86 and HSE06; atomization energy was calculated using rPW86. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

ColabFit ID: JARVIS-Polymer-Genome_HuanMannodi-KanakkithodiKimSharmaPilaniaRamprasad__DS_dbgckv1il6v7_0
Name: JARVIS_Polymer-Genome
Authors: Tran Doan Huan, Arun Mannodi-Kanakkithodi, Chiho Kim, Vinit Sharma, Ghanshyam Pilania, Rampi Ramprasad
Elements: C, Cd, H, O, N, S, Ca, Sn, Zn, F, Mg, Al, Cl, Ti, Zr, Hf, Pb
Number of Configurations: 1,073
Number of Elements: 17
Number of Atoms: 34,441
Property Types: atomization-energy, band-gap

Links:
https://ndownloader.figshare.com/files/28682010
https://doi.org/10.1038/sdata.2016.12
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The QE-TB dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset contains configurations generated in Quantum ESPRESSO. JARVIS is a set of tools and datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_QE_TB_GarrityChoudhary__DS_e471qdt7c6db_0
Name: JARVIS_QE_TB
Authors: Kevin F. Garrity, Kamal Choudhary
Elements: N, W, Y, Ni, S, Cs, Rh, Cl, Cr, Tl, Be, Bi, Ti, Mg, Sc, Mn, Re, Se, Si, Cu, H, O, P, Co, Ga, Zr, As, Ba, K, Ag, I, Ca, Al, Zn, Nb, Hg, Sb, Cd, Ir, Te, Pt, Pd, C, Tc, Li, Pb, Os, Br, Ru, In, Fe, Au, B, Ge, V, Hf, F, Ta, Na, Mo, Sr, Rb, La, Sn
Number of Configurations: 828,478
Number of Elements: 64
Number of Atoms: 2,575,418
Property Types: potential-energy, formation-energy, cauchy-stress, band-gap, atomic-forces

Links:
https://ndownloader.figshare.com/files/29070555
https://doi.org/10.1103/PhysRevMaterials.7.044603
https://jarvis.nist.gov/
https://arxiv.org/abs/2112.11585

Download Dataset XYZ file Description: The JARVIS_QM9_STD_JCTC dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the QM9 dataset, originally created as part of the datasets at quantum-machine.org. Units for r2 (electronic spatial extent) are a^2; for alpha (isotropic polarizability), a^3; for mu (dipole moment), D; for Cv (heat capacity), cal/mol K. Units for all other properties are eV. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.For the first iteration of DFT calculations, Gaussian 09’s default electronic and geometry thresholds have been used for all molecules. For those molecules which failed to reach SCF convergence ultrafine grids have been invoked within a second iteration for evaluating the XC energy contributions. Within a third iteration on the remaining unconverged molecules, we identified those which had relaxed to saddle points, and further tightened the SCF criteria using the keyword scf(maxcycle=200, verytight). All those molecules which still featured imaginary frequencies entered the fourth iteration using keywords, opt(calcfc, maxstep=5, maxcycles=1000). calcfc constructs a Hessian in the first step of the geometry relaxation for eigenvector following. Within the fifth and final iteration, all molecules which still failed to reach convergence, have subsequently been converged using opt(calcall, maxstep=1, maxcycles=1000)

ColabFit ID: JARVIS_QM9_STD_JCTC_RamakrishnanDralRuppLilienfeld__DS_jz1q9juw7ycj_0
Name: JARVIS_QM9_STD_JCTC
Authors: Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld
Elements: C, H, N, O, F
Number of Configurations: 130,829
Number of Elements: 5
Number of Atoms: 2,359,192
Property Types: potential-energy, free-energy, band-gap

Links:
https://ndownloader.figshare.com/files/28715319
https://doi.org/10.1038/sdata.2014.22
https://jarvis.nist.gov/
http://quantum-machine.org/datasets/

Download Dataset XYZ file Description: The JARVIS-QM9-DGL dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the QM9 dataset, originally created as part of the datasets at quantum-machine.org, as implemented with the Deep Graph Library (DGL) Python package. Units for r2 (electronic spatial extent) are a0^2; for alpha (isotropic polarizability), a0^3; for mu (dipole moment), D; for Cv (heat capacity), cal/mol K. Units for all other properties are eV. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

ColabFit ID: JARVIS-QM9-DGL_RamakrishnanDralRuppLilienfeld__DS_tat5i46x3hkr_0
Name: JARVIS_QM9-DGL
Authors: Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld
Elements: C, H, N, O, F
Number of Configurations: 130,831
Number of Elements: 5
Number of Atoms: 2,358,210
Property Types: potential-energy, free-energy, band-gap

Links:
https://ndownloader.figshare.com/files/28541196
https://doi.org/10.1038/sdata.2014.22
https://jarvis.nist.gov/
http://quantum-machine.org/datasets/
https://docs.dgl.ai/en/0.8.x/generated/dgl.data.QM9Dataset.html

Download Dataset XYZ file Description: The JARVIS_QMOF dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the Quantum Metal-Organic Frameworks (QMOF) dataset, comprising quantum-chemical properties for >14,000 experimentally synthesized MOFs. QMOF contains "DFT-ready" data: filtered to remove omitted, overlapping, unbonded or deleted atoms, along with other kinds of problematic structures commented on in the literature. Data were generated via high-throughput DFT workflow, at the PBE-D3(BJ) level of theory using VASP software. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_QMOF_RosenIyerRayYaoAspuru-GuzikGagliardiNotesteinSnurr__DS_221svb9fxfk7_0
Name: JARVIS_QMOF
Authors: Andrew S. Rosen, Shaelyn M. Iyer, Debmalya Ray, Zhenpeng Yao, Alán Aspuru-Guzik, Laura Gagliardi, Justin M. Notestein, Randall Q. Snurr
Elements: C, Cu, H, N, O, Pr, S, Gd, Br, Cl, Zn, Ag, Co, Fe, Mn, F, Cd, I, Al, Li, P, Zr, In, Au, K, Hg, Sn, Ni, Tb, Na, Mo, Ho, Pb, Sr, Sb, V, Yb, Dy, Mg, Er, La, Y, Cs, Sm, Pd, Nd, Rh, Si, Ba, B, W, Bi, U, Rb, Ti, Eu, Tl, Ru, Ca, Se, Ga, Hf, As, Ir, Cr, Ce, Pt, Lu, Te, Tc, Tm, Re, Th, Ge, Sc, Nb, Pu, Be, Np
Number of Configurations: 20,425
Number of Elements: 79
Number of Atoms: 2,321,633
Property Types: potential-energy, band-gap

Links:
https://figshare.com/ndownloader/files/30972640
https://doi.org/10.1016/j.matt.2021.02.015
https://jarvis.nist.gov/

Download Dataset XYZ file Description: The JARVIS_SNUMAT dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains band gap data for >10,000 materials, computed using a hybrid functional and considering the stable magnetic ordering. Structure relaxation and band edges are obtained using the PBE XC functional; band gap energy is subsequently obtained using the HSE06 hybrid functional. Optical and fundamental band gap energies are included. Some gap energies are recalculated by including spin-orbit coupling. These are noted in the band gap metadata as "SOC=true". JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_SNUMAT_KimLeeHongYoonAnLeeJeongYooKangYounHan__DS_1nbddfnjxbjc_0
Name: JARVIS_SNUMAT
Authors: Sangtae Kim, Miso Lee, Changho Hong, Youngchae Yoon, Hyungmin An, Dongheon Lee, Wonseok Jeong, Dongsun Yoo, Youngho Kang, Yong Youn, Seungwu Han
Elements: As, Cu, Se, Ba, S, Au, O, Rb, Cs, P, V, Cd, Na, C, F, Te, H, Hg, N, Nb, Sr, Mn, Mo, Sb, K, I, Ti, Cl, Hf, Ge, Pt, Pd, Ag, Bi, Pb, B, Al, Li, Ca, Ni, Ce, Tl, Si, Ir, Zn, Cr, La, Sn, In, Lu, Br, Ru, Ga, Re, Y, Zr, Fe, Be, Co, Mg, Os, W, Rh, Ta, He, Sc, Xe, Kr, Dy, Tc, Th, Ne, Ar
Number of Configurations: 10,481
Number of Elements: 73
Number of Atoms: 216,749
Property Types: band-gap

Links:
https://ndownloader.figshare.com/files/38521736
https://doi.org/10.1038/s41597-020-00723-8
https://jarvis.nist.gov/
https://www.snumat.com/

Download Dataset XYZ file Description: The JARVIS_TinNet dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the TinNet-N dataset: a collection assembled to train a machine learning model for the purposes of assisting catalyst design by predicting chemical reactivity of transition-metal surfaces. The adsorption systems contained in this dataset consist of {100}-terminated Pt-based bimetallic surfaces doped with a third element. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_TinNet_N_WangPillaiWangAchenieXin__DS_ffsgl0wufoft_0
Name: JARVIS_TinNet_N
Authors: Shih-Han Wang, Hemanth Somarajan Pillai, Siwen Wang, Luke E. K. Achenie, Hongliang Xin
Elements: H, N, Ni, O, Pt, Zn, Pd, Fe, Ru, Au, Os, Cu, Ir, Mo, Cr, Rh, Co, Tc, Re, Ag, Mn, W, V, Cd, Nb, Sc, Hf
Number of Configurations: 329
Number of Elements: 27
Number of Atoms: 6,251
Property Types: adsorption-energy

Links:
https://figshare.com/ndownloader/files/40934285
https://doi.org/10.1038/s41467-021-25639-8
https://jarvis.nist.gov/
https://github.com/hlxin/tinnet/tree/master

Download Dataset XYZ file Description: The JARVIS_TinNet dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the TinNet-O dataset: a collection assembled to train a machine learning model for the purposes of assisting catalyst design by predicting chemical reactivity of transition-metal surfaces. The adsorption systems contained in this dataset consist of {111}-terminated metal surfaces. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_TinNet_O_WangPillaiWangAchenieXin__DS_ok9dbnj53zih_0
Name: JARVIS_TinNet_O
Authors: Shih-Han Wang, Hemanth Somarajan Pillai, Siwen Wang, Luke E. K. Achenie, Hongliang Xin
Elements: O, Pt, V, Fe, Ni, Ru, Rh, Au, Ta, Mo, Co, Os, Pd, Sc, W, Cd, Cu, Re, Ir, Cr, Mn, Zn, Pb, Sn, Y, Ag, Al, Ga, Bi, Ti, Nb, Tl, Hf, In, La, Zr
Number of Configurations: 747
Number of Elements: 36
Number of Atoms: 12,699
Property Types: adsorption-energy

Links:
https://figshare.com/ndownloader/files/40934285
https://doi.org/10.1038/s41467-021-25639-8
https://jarvis.nist.gov/
https://github.com/hlxin/tinnet/tree/master

Download Dataset XYZ file Description: The JARVIS_TinNet dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the TinNet-OH dataset: a collection assembled to train a machine learning model for the purposes of assisting catalyst design by predicting chemical reactivity of transition-metal surfaces. The adsorption systems contained in this dataset consist of {111}-terminated metal surfaces. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.

ColabFit ID: JARVIS_TinNet_OH_WangPillaiWangAchenieXin__DS_mk2e641eop9w_0
Name: JARVIS_TinNet_OH
Authors: Shih-Han Wang, Hemanth Somarajan Pillai, Siwen Wang, Luke E. K. Achenie, Hongliang Xin
Elements: Al, H, O, Ru, Ir, Pd, Cr, Cu, Pt, Rh, Re, Sc, Os, Ta, Mo, Ni, W, V, Au, Tl, Fe, Zn, Sn, Ag, Pb, Co, Ti, Nb, Y, Mn, Zr, La, Ga, Bi, In, Hf, Cd
Number of Configurations: 748
Number of Elements: 37
Number of Atoms: 13,464
Property Types: adsorption-energy

Links:
https://figshare.com/ndownloader/files/40934285
https://doi.org/10.1038/s41467-021-25639-8
https://jarvis.nist.gov/
https://github.com/hlxin/tinnet/tree/master

Download Dataset XYZ file Description: Approximately 6,500 configurations of Li10GeP2S12, based on crystal structures from the Materials Project database, material ID mp-696129. One of two LiGePS datasets from this source. The other uses the PBEsol functional, rather than the PBE functional.

ColabFit ID: LiGePS_SSE_PBE_HuangZhangWangZhaoChengE__DS_4lev0d7cs1yl_0
Name: LiGePS_SSE_PBE
Authors: Jianxing Huang, Linfeng Zhang, Han Wang, Jinbao Zhao, Jun Cheng, Weinan E
Elements: Ge, Li, P, S
Number of Configurations: 6,550
Number of Elements: 4
Number of Atoms: 1,479,000
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://www.aissquare.com/datasets/detail?pageType=datasets&name=LiGePS-SSE-PBE
https://doi.org/10.1063/5.0041849

Download Dataset XYZ file Description: Approximately 2,800 configurations of Li10GeP2S12, based on crystal structures from the Materials Project database, material ID mp-696129. One of two LiGePS datasets from this source. The other uses the PBE functional, rather than the PBEsol functional.

ColabFit ID: LiGePS_SSE_PBEsol_HuangZhangWangZhaoChengE__DS_dhtzh0y2108p_0
Name: LiGePS_SSE_PBEsol
Authors: Jianxing Huang, Linfeng Zhang, Han Wang, Jinbao Zhao, Jun Cheng, Weinan E
Elements: Ge, Li, P, S
Number of Configurations: 2,835
Number of Elements: 4
Number of Atoms: 504,350
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://www.aissquare.com/datasets/detail?pageType=datasets&name=LiGePS-SSE-PBEsol
https://doi.org/10.1063/5.0041849

Download Dataset XYZ file Description: We establish the sign of the linear magnetoelectric (ME) coefficient, α, in chromia, Cr₂O₃. Cr₂O₃ is the prototypical linear ME material, in which an electric (magnetic) field induces a linearly proportional magnetization (polarization), and a single magnetic domain can be selected by annealing in combined magnetic (H) and electric (E) fields. Opposite antiferromagnetic domains have opposite ME responses, and which antiferromagnetic domain corresponds to which sign of response has previously been unclear. We use density functional theory (DFT) to calculate the magnetic response of a single antiferromagnetic domain of Cr₂O₃ to an applied in-plane electric field at 0 K. We find that the domain with nearest neighbor magnetic moments oriented away from (towards) each other has a negative (positive) in-plane ME coefficient, α⊥, at 0 K. We show that this sign is consistent with all other DFT calculations in the literature that specified the domain orientation, independent of the choice of DFT code or functional, the method used to apply the field, and whether the direct (magnetic field) or inverse (electric field) ME response was calculated. Next, we reanalyze our previously published spherical neutron polarimetry data to determine the antiferromagnetic domain produced by annealing in combined E and H fields oriented along the crystallographic symmetry axis at room temperature. We find that the antiferromagnetic domain with nearest-neighbor magnetic moments oriented away from (towards) each other is produced by annealing in (anti-)parallel E and H fields, corresponding to a positive (negative) axial ME coefficient, α∥, at room temperature. Since α⊥ at 0 K and α∥ at room temperature are known to be of opposite sign, our computational and experimental results are consistent. This dataset contains the input data to reproduce the calculation of the magnetoelectric effect as plotted in Fig. 3 of the manuscript, for Elk, Vasp, and Quantum Espresso.

ColabFit ID: linear_magnetic_coefficient_in_Cr2O3_JPCM2024_BousquetLelievre-BernaQureshiSohSpaldinUrruVerbeekWeber__DS_82x5bfiiyaij_0
Name: linear_magnetic_coefficient_in_Cr2O3_JPCM2024
Authors: Eric Bousquet, Eddy Lelièvre-Berna, Navid Qureshi, Jian-Rui Soh, Nicola Ann Spaldin, Andrea Urru, Xanthe Henderike Verbeek, Sophie Francis Weber
Elements: Cr, O
Number of Configurations: 165
Number of Elements: 2
Number of Atoms: 1,650
Property Types: potential-energy, cauchy-stress, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:ek-fp
http://doi.org/10.1088/1361-648X/ad1a59

Download Dataset XYZ file Description: Approximately 9,100 configurations of Li10SiP2S12, based on crystal structures from the Materials Project database, material ID mp-696129. One of two LiSiPS datasets from this source. The other uses the PBEsol functional, rather than the PBE functional.

ColabFit ID: LiSiPS_SSE_PBE_HuangZhangWangZhaoChengE__DS_9p3sip4yhiju_0
Name: LiSiPS_SSE_PBE
Authors: Jianxing Huang, Linfeng Zhang, Han Wang, Jinbao Zhao, Jun Cheng, Weinan E
Elements: Li, P, S, Si
Number of Configurations: 9,169
Number of Elements: 4
Number of Atoms: 2,101,550
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://www.aissquare.com/datasets/detail?pageType=datasets&name=LiSiPS-SSE-PBE
https://doi.org/10.1063/5.0041849

Download Dataset XYZ file Description: Approximately 2,300 configurations of Li10SiP2S12, based on crystal structures from the Materials Project database, material ID mp-696129. One of two LiSiPS datasets from this source. The other uses the PBE functional, rather than the PBEsol functional.

ColabFit ID: LiSiPS-SSE-PBEsol_HuangZhangWangZhaoChengE__DS_th9w66lyxspv_0
Name: LiSiPS_SSE_PBEsol
Authors: Jianxing Huang, Linfeng Zhang, Han Wang, Jinbao Zhao, Jun Cheng, Weinan E
Elements: Li, P, S, Si
Number of Configurations: 2,357
Number of Elements: 4
Number of Atoms: 313,150
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://www.aissquare.com/datasets/detail?pageType=datasets&name=LiSiPS-SSE-PBEsol
https://doi.org/10.1063/5.0041849

Download Dataset XYZ file Description: This dataset contains configurations of lithium titanate from the publication Kinetic Pathways of ionic transport in fast-charging lithium titanate. In order to understand the origin of various EELS (electron energy-loss spectroscopy) spectra features, EELS spectra were simulated using the Vienna Ab initio Simulation (VASP) package. For a specific Li in a given configuration, this is done by calculating the DOS and integrated DOS considering a Li core-hole on the position of the specific Li and calculating the EELS based on the DOS. The minimum energy paths (MEP) and migration energy of Li were calculated in various compositions, including Li4Ti5O12 with an additional Li carrier, Li5Ti5O12 with an additional Li carrier, and Li7Ti5O12 with a Li vacancy carrier.

ColabFit ID: LiTiO_Science_2020_ChenSeo__DS_0pv4lpmx2ov3_0
Name: LiTiO_Science_2020
Authors: Tina Chen, Dong-hwa Seo
Elements: Li, O, Ti, Be
Number of Configurations: 849
Number of Elements: 4
Number of Atoms: 150,105
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.24435/materialscloud:2020.0006/v1
https://doi.org/10.1126/science.aax3520

Download Dataset XYZ file Description: This dataset contains structural calculations of LaMnO3 carried out in Quantum ESPRESSO at the DFT-PBEsol+U level of theory. The dataset was built to explore strained and stoichiometric and oxygen-deficient LaMnO3.

ColabFit ID: local_polarization_in_oxygen-deficient_LaMnO3_PRR2020_RiccaNiederhauserAschauer__DS_t8goqf2uglhj_0
Name: local_polarization_in_oxygen-deficient_LaMnO3_PRR2020
Authors: Chiara Ricca, Nicolas Niederhauser, Ulrich Aschauer
Elements: La, O, Mn, Ba, Ti
Number of Configurations: 4,514
Number of Elements: 5
Number of Atoms: 174,337
Property Types: atomic-forces, potential-energy, cauchy-stress

Links:
https://doi.org/10.24435/materialscloud:m9-9d
http://doi.org/10.1103/PhysRevResearch.2.042040

Download Dataset XYZ file Description: Matbench v0.1 test dataset for predicting DFT formation energy from structure. Adapted from Materials Project database. Entries having formation energy more than 2.5eV and those containing noble gases are removed. Retrieved April 2, 2019. For benchmarking w/ nested cross validation, the order of the dataset must be identical to the retrieved data; refer to the Automatminer/Matbench publication for more details.Matbench is an automated leaderboard for benchmarking state of the art ML algorithms predicting a diverse range of solid materials' properties. It is hosted and maintained by the Materials Project.

ColabFit ID: Matbench_mp_e_form_DunnWangGanoseDoppJain__DS_5drebe4tktiu_0
Name: Matbench_mp_e_form
Authors: Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp, Anubhav Jain
Elements: Bi, Mg, O, Rb, In, Sn, C, Fe, K, N, F, Mn, Ca, S, Lu, Re, Co, Cu, Zn, Rh, Si, Sm, Dy, Cl, H, Mo, Ni, Ge, Na, V, Al, As, Sr, Ga, Y, Li, Ti, Ta, U, Nb, Se, P, I, Cs, Tm, W, Zr, Ba, Ru, Ce, Tl, Ir, Yb, Pb, Sb, B, Hf, Cd, Pd, Sc, Ag, La, Nd, Au, Ho, Er, Be, Pr, Br, Te, Pm, Cr, Pt, Tb, Tc, Gd, Hg, Np, Th, Os, Ac, Eu, Pu, Pa
Number of Configurations: 132,752
Number of Elements: 84
Number of Atoms: 3,869,658
Property Types: formation-energy

Links:
https://matbench.materialsproject.org/
https://doi.org/10.1038/s41524-020-00406-3
https://doi.org/10.1016/j.commatsci.2014.10.037

Download Dataset XYZ file Description: The Matbench_mp_gap dataset is a Matbench v0.1 test dataset for predicting DFT PBE band gap from structure, adapted from the Materials Project database. Entries having a formation energy (or energy above the convex hull) greater than 150meV and those containing noble gases have been removed. Retrieved April 2, 2019. Refer to the Automatminer/Matbench publication for more details. This dataset contains band gap as calculated by PBE DFT from the Materials Project, in eV. Matbench is an automated leaderboard for benchmarking state of the art ML algorithms predicting a diverse range of solid materials' properties. It is hosted and maintained by the Materials Project.

ColabFit ID: Matbench_mp_gap_DunnWangGanoseDoppJain__DS_6lq2f26dluql_0
Name: Matbench_mp_gap
Authors: Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp, Anubhav Jain
Elements: Cu, Li, Mn, O, P, B, Ho, Ni, Co, Er, Ir, Pr, Y, As, K, Sn, W, F, Fe, N, Nd, Se, V, Ag, Pb, Tm, Cr, Ga, Pt, In, Rh, U, Ca, Tl, Lu, Mg, Zn, Au, Ru, Al, Pd, C, Ge, H, Cl, Br, Rb, Cs, Ti, Ba, Te, Pm, Zr, Hg, I, Sb, S, Si, Cd, Sr, Pa, Bi, Dy, Gd, Re, La, Nb, Sm, Ta, Na, Tb, Mo, Yb, Sc, Ac, Be, Hf, Os, Np, Th, Eu, Pu, Ce, Tc
Number of Configurations: 106,113
Number of Elements: 84
Number of Atoms: 3,184,984
Property Types: band-gap

Links:
https://matbench.materialsproject.org/
https://doi.org/10.1039/C2EE22341D

Download Dataset XYZ file Description: The Matbench_perovskites dataset is a Matbench v0.1 test dataset for predicting formation energy from crystal structure. Adapted from an original dataset generated by Castelli et al. Refer to the Automatminer/Matbench publication for more details. This dataset contains the energy of formation of the entire 5-atom perovskite cell in eV as calculated by RPBE GGA-DFT. Note the reference state for oxygen was computed from oxygen's chemical potential in water vapor, not as oxygen molecules, to reflect the application for which these perovskites were studied. Matbench is an automated leaderboard for benchmarking state of the art ML algorithms predicting a diverse range of solid materials' properties. It is hosted and maintained by the Materials Project.

ColabFit ID: Matbench_perovskites_DunnWangGanoseDoppJain__DS_sinisy47w8ff_0
Name: Matbench_perovskites
Authors: Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp, Anubhav Jain
Elements: O, Pt, S, W, Li, Te, Ga, Ti, K, Sn, Ag, N, Cs, F, Sr, Ta, In, Ba, Ca, Ni, Ge, Pd, Zr, Hf, Zn, Cu, Mo, Na, Be, Co, Cr, B, Re, Sc, Al, Bi, Os, Fe, La, Ir, Mg, Au, Hg, Sb, Ru, Y, As, Pb, Rh, Tl, Rb, Nb, Mn, Cd, V, Si
Number of Configurations: 18,928
Number of Elements: 56
Number of Atoms: 94,640
Property Types: formation-energy

Links:
https://matbench.materialsproject.org/
https://doi.org/10.1039/C2EE22341D

Download Dataset XYZ file Description: Configurations from the Materials Project database: an online resource with the goal of computing properties of all inorganic materials.

ColabFit ID: Materials Project_JainOngHautierChenRichardsDacekCholiaGunterSkinnerCederPersson__DS_pv1f3dlo5dsc_0
Name: Materials_Project
Authors: Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson
Elements: Co, Li, Mn, O, Er, Ge, Na, Cs, W, Fe, N, C, Mo, Si, F, V, P, Pb, Al, Te, Cr, Sc, H, Ti, Ag, La, Mg, Au, Cu, Zr, Ca, Ta, Rb, S, Cl, Re, U, Ba, Nb, Y, Sr, Cd, Br, Se, In, Sm, Bi, Ni, K, Zn, I, Hg, Sn, B, Ce, Th, Eu, Pt, Ga, Pd, Pr, Sb, Gd, Nd, Ir, Tl, As, Ru, Yb, Rh, Lu, Hf, Dy, Tm, Pu, Tb, Tc, Be, Np, Ho, Pm, Os, Ac, Pa, Xe, Kr, He, Ar, Ne
Number of Configurations: 6,342,530
Number of Elements: 89
Number of Atoms: 200,016,161
Property Types: free-energy, atomic-forces, cauchy-stress, band-gap

Links:
https://materialsproject.org
https://doi.org/10.1063/1.4812323

Download Dataset XYZ file Description: Configurations of water, acetonitrile and methanol, simulated with ASE and modeled using a variety of software and methods: GAP, SchNet, GDML, ORCA and mbGDML. Forces and potential energy included; metadata includes kinetic energy and velocities.

ColabFit ID: mbGDML_maldonado_2023_Maldonado__DS_e94my2wrh074_0
Name: mbGDML_maldonado_2023
Authors: Alex M. Maldonado
Elements: H, O, C, N
Number of Configurations: 24,543
Number of Elements: 4
Number of Atoms: 712,134
Property Types: potential-energy, atomic-forces

Links:
https://doi.org/10.5281/zenodo.7112197
https://doi.org/10.26434/chemrxiv-2023-wdd1r

Download Dataset XYZ file Description: Dataset containing MD trajectories of the 42-atom tetrapeptide Ac-Ala3-NHMe from the MD22 benchmark set. MD22 represents a collection of datasets in a benchmark that can be considered an updated version of the MD17 benchmark datasets, including more challenges with respect to system size, flexibility and degree of non-locality. The datasets in MD22 include MD trajectories of the protein Ac-Ala3-NHMe; the lipid DHA (docosahexaenoic acid); the carbohydrate stachyose; nucleic acids AT-AT and AT-AT-CG-CG; and the buckyball catcher and double-walled nanotube supramolecules. Each of these is included here in a separate dataset, as represented on sgdml.org. Calculations were performed using FHI-aims and i-Pi software at the DFT-PBE+MBD level of theory. Trajectories were sampled at temperatures between 400-500 K at 1 fs resolution.

ColabFit ID: MD22_Ac_Ala3_NHMe_ChmielaVassilev-GalindoUnkeKabyldaSaucedaTkatchenkoMuller__DS_l6awe3jnivnm_0
Name: MD22_Ac_Ala3_NHMe
Authors: Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T. Unke, Adil Kabylda, Huziel E. Sauceda, Alexandre Tkatchenko, Klaus-Robert Müller
Elements: C, H, N, O
Number of Configurations: 85,109
Number of Elements: 4
Number of Atoms: 3,574,578
Property Types: potential-energy, atomic-forces

Links:
http://sgdml.org/
https://doi.org/10.1126/sciadv.adf0873

Download Dataset XYZ file Description: Dataset containing MD trajectories of AT-AT DNA base pairs from the MD22 benchmark set. {DESC}

ColabFit ID: MD22_AT_AT_ChmielaVassilev-GalindoUnkeKabyldaSaucedaTkatchenkoMuller__DS_eqt5dbhsmm68_0
Name: MD22_AT_AT
Authors: Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T. Unke, Adil Kabylda, Huziel E. Sauceda, Alexandre Tkatchenko, Klaus-Robert Müller
Elements: C, H, N, O
Number of Configurations: 20,001
Number of Elements: 4
Number of Atoms: 1,200,060
Property Types: potential-energy, atomic-forces

Links:
http://sgdml.org/
https://doi.org/10.1126/sciadv.adf0873

Download Dataset XYZ file Description: Dataset containing MD trajectories of AT-AT-CG-CG DNA base pairs from the MD22 benchmark set. MD22 represents a collection of datasets in a benchmark that can be considered an updated version of the MD17 benchmark datasets, including more challenges with respect to system size, flexibility and degree of non-locality. The datasets in MD22 include MD trajectories of the protein Ac-Ala3-NHMe; the lipid DHA (docosahexaenoic acid); the carbohydrate stachyose; nucleic acids AT-AT and AT-AT-CG-CG; and the buckyball catcher and double-walled nanotube supramolecules. Each of these is included here in a separate dataset, as represented on sgdml.org. Calculations were performed using FHI-aims and i-Pi software at the DFT-PBE+MBD level of theory. Trajectories were sampled at temperatures between 400-500 K at 1 fs resolution.

ColabFit ID: MD22_AT_AT_CG_CG_ChmielaVassilev-GalindoUnkeKabyldaSaucedaTkatchenkoMuller__DS_rx1ei5q0x9gy_0
Name: MD22_AT_AT_CG_CG
Authors: Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T. Unke, Adil Kabylda, Huziel E. Sauceda, Alexandre Tkatchenko, Klaus-Robert Müller
Elements: C, H, N, O
Number of Configurations: 10,153
Number of Elements: 4
Number of Atoms: 1,198,054
Property Types: potential-energy, atomic-forces

Links:
http://sgdml.org/
https://doi.org/10.1126/sciadv.adf0873

Download Dataset XYZ file Description: Dataset containing MD trajectories of the buckyball-catcher supramolecule from the MD22 benchmark set. MD22 represents a collection of datasets in a benchmark that can be considered an updated version of the MD17 benchmark datasets, including more challenges with respect to system size, flexibility and degree of non-locality. The datasets in MD22 include MD trajectories of the protein Ac-Ala3-NHMe; the lipid DHA (docosahexaenoic acid); the carbohydrate stachyose; nucleic acids AT-AT and AT-AT-CG-CG; and the buckyball catcher and double-walled nanotube supramolecules. Each of these is included here in a separate dataset, as represented on sgdml.org. Calculations were performed using FHI-aims and i-Pi software at the DFT-PBE+MBD level of theory. Trajectories were sampled at temperatures between 400-500 K at 1 fs resolution.

ColabFit ID: MD22_buckyball_catcher_ChmielaVassilev-GalindoUnkeKabyldaSaucedaTkatchenkoMuller__DS_7g8q68fszjvs_0
Name: MD22_buckyball_catcher
Authors: Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T. Unke, Adil Kabylda, Huziel E. Sauceda, Alexandre Tkatchenko, Klaus-Robert Müller
Elements: C, H
Number of Configurations: 6,102
Number of Elements: 2
Number of Atoms: 903,096
Property Types: potential-energy, atomic-forces

Links:
http://sgdml.org/
https://doi.org/10.1126/sciadv.adf0873

Download Dataset XYZ file Description: Dataset containing MD trajectories of DHA (docosahexaenoic acid) from the MD22 benchmark set. MD22 represents a collection of datasets in a benchmark that can be considered an updated version of the MD17 benchmark datasets, including more challenges with respect to system size, flexibility and degree of non-locality. The datasets in MD22 include MD trajectories of the protein Ac-Ala3-NHMe; the lipid DHA (docosahexaenoic acid); the carbohydrate stachyose; nucleic acids AT-AT and AT-AT-CG-CG; and the buckyball catcher and double-walled nanotube supramolecules. Each of these is included here in a separate dataset, as represented on sgdml.org. Calculations were performed using FHI-aims and i-Pi software at the DFT-PBE+MBD level of theory. Trajectories were sampled at temperatures between 400-500 K at 1 fs resolution.

ColabFit ID: MD22_DHA_ChmielaVassilev-GalindoUnkeKabyldaSaucedaTkatchenkoMuller__DS_xwmthngerf71_0
Name: MD22_DHA
Authors: Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T. Unke, Adil Kabylda, Huziel E. Sauceda, Alexandre Tkatchenko, Klaus-Robert Müller
Elements: C, H, O
Number of Configurations: 69,753
Number of Elements: 3
Number of Atoms: 3,906,168
Property Types: potential-energy, atomic-forces

Links:
http://sgdml.org/
https://doi.org/10.1126/sciadv.adf0873

Download Dataset XYZ file Description: Dataset containing MD trajectories of the double-walled nanotube supramolecule from the MD22 benchmark set. MD22 represents a collection of datasets in a benchmark that can be considered an updated version of the MD17 benchmark datasets, including more challenges with respect to system size, flexibility and degree of non-locality. The datasets in MD22 include MD trajectories of the protein Ac-Ala3-NHMe; the lipid DHA (docosahexaenoic acid); the carbohydrate stachyose; nucleic acids AT-AT and AT-AT-CG-CG; and the buckyball catcher and double-walled nanotube supramolecules. Each of these is included here in a separate dataset, as represented on sgdml.org. Calculations were performed using FHI-aims and i-Pi software at the DFT-PBE+MBD level of theory. Trajectories were sampled at temperatures between 400-500 K at 1 fs resolution.

ColabFit ID: MD22_double_walled_nanotube_ChmielaVassilev-GalindoUnkeKabyldaSaucedaTkatchenkoMuller__DS_d92bnafzwynr_0
Name: MD22_double_walled_nanotube
Authors: Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T. Unke, Adil Kabylda, Huziel E. Sauceda, Alexandre Tkatchenko, Klaus-Robert Müller
Elements: C, H
Number of Configurations: 5,032
Number of Elements: 2
Number of Atoms: 1,861,840
Property Types: potential-energy, atomic-forces

Links:
http://sgdml.org/
https://doi.org/10.1126/sciadv.adf0873

Download Dataset XYZ file Description: Dataset containing MD trajectories of the tetrasaccharide stachyose from the MD22 benchmark set. MD22 represents a collection of datasets in a benchmark that can be considered an updated version of the MD17 benchmark datasets, including more challenges with respect to system size, flexibility and degree of non-locality. The datasets in MD22 include MD trajectories of the protein Ac-Ala3-NHMe; the lipid DHA (docosahexaenoic acid); the carbohydrate stachyose; nucleic acids AT-AT and AT-AT-CG-CG; and the buckyball catcher and double-walled nanotube supramolecules. Each of these is included here in a separate dataset, as represented on sgdml.org. Calculations were performed using FHI-aims and i-Pi software at the DFT-PBE+MBD level of theory. Trajectories were sampled at temperatures between 400-500 K at 1 fs resolution.

ColabFit ID: MD22_stachyose_ChmielaVassilev-GalindoUnkeKabyldaSaucedaTkatchenkoMuller__DS_87ojug1k96ef_0
Name: MD22_stachyose
Authors: Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T. Unke, Adil Kabylda, Huziel E. Sauceda, Alexandre Tkatchenko, Klaus-Robert Müller
Elements: C, H, O
Number of Configurations: 27,272
Number of Elements: 3
Number of Atoms: 2,372,664
Property Types: potential-energy, atomic-forces

Links:
http://sgdml.org/
https://doi.org/10.1126/sciadv.adf0873

Download Dataset XYZ file Description: 16748 configurations of magnesium with gathered energy, stress and forces at the DFT level of theory.

ColabFit ID: Mg_edmonds_2022_Poul__DS_caktb6z8yiy7_0
Name: Mg_edmonds_2022
Authors: Marvin Poul
Elements: Mg
Number of Configurations: 16,874
Number of Elements: 1
Number of Atoms: 78,617
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://doi.org/10.17617/3.A3MB7Z
https://doi.org/10.1103/PhysRevB.107.104103

Download Dataset XYZ file Description: Example dataset for MISPR (Materials Informatics for Structure-Property Relationships) materials science simulation software, with DFT-calculated configuration properties for three different MISPR workflows: nuclear magnetic resonance (NMR) chemical shifts, electrostatic partial charges (ESP) and bond dissociation energies (BDE).

ColabFit ID: MISPR_AtwiBlissMakeevRajput__DS_rsao7xrpu9ig_0
Name: MISPR
Authors: Rasha Atwi, Matthew Bliss, Maxim Makeev, Nav Nidhi Rajput
Elements: C, F, H, O, Si, N, S, Cl, P
Number of Configurations: 503
Number of Elements: 9
Number of Atoms: 8,996
Property Types: potential-energy

Links:
https://doi.org/10.1038/s41598-022-20009-w
https://github.com/rashatwi/mispr-dataset

Download Dataset XYZ file Description: A comprehensive DFT data set was generated for six elements - Li, Mo, Ni, Cu, Si, and Ge. These elements were chosen to span a variety of chemistries (main group metal, transition metal, and semiconductor), crystal structures (bcc, fcc, and diamond) and bonding types (metallic and covalent). This dataset comprises only the Cu configurations

ColabFit ID: mlearn_Cu_test_ZuoChenLiDengChenBehlerCsanyiShapeevThompsonWoodOng__DS_xmgn3ofqzon9_0
Name: mlearn_Cu_test
Authors: Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, Shyue Ping Ong
Elements: Cu
Number of Configurations: 31
Number of Elements: 1
Number of Atoms: 3,178
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/materialsvirtuallab/mlearn
https://doi.org/10.1021/acs.jpca.9b08723

Download Dataset XYZ file Description: A comprehensive DFT data set was generated for six elements - Li, Mo, Ni, Cu, Si, and Ge. These elements were chosen to span a variety of chemistries (main group metal, transition metal, and semiconductor), crystal structures (bcc, fcc, and diamond) and bonding types (metallic and covalent). This dataset comprises only the Cu configurations

ColabFit ID: mlearn_Cu_train_ZuoChenLiDengChenBehlerCsanyiShapeevThompsonWoodOng__DS_3pv3hck35iy6_0
Name: mlearn_Cu_train
Authors: Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, Shyue Ping Ong
Elements: Cu
Number of Configurations: 262
Number of Elements: 1
Number of Atoms: 27,416
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/materialsvirtuallab/mlearn/tree/master/data
https://doi.org/10.1021/acs.jpca.9b08723

Download Dataset XYZ file Description: A comprehensive DFT data set was generated for six elements - Li, Mo, Ni, Cu, Si, and Ge. These elements were chosen to span a variety of chemistries (main group metal, transition metal, and semiconductor), crystal structures (bcc, fcc, and diamond) and bonding types (metallic and covalent). This dataset comprises only the Ge configurations

ColabFit ID: mlearn_Ge_test_ZuoChenLiDengChenBehlerCsanyiShapeevThompsonWoodOng__DS_pyrk84w3auvb_0
Name: mlearn_Ge_test
Authors: Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, Shyue Ping Ong
Elements: Ge
Number of Configurations: 25
Number of Elements: 1
Number of Atoms: 1,568
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/materialsvirtuallab/mlearn
https://doi.org/10.1021/acs.jpca.9b08723

Download Dataset XYZ file Description: A comprehensive DFT data set was generated for six elements - Li, Mo, Ni, Cu, Si, and Ge. These elements were chosen to span a variety of chemistries (main group metal, transition metal, and semiconductor), crystal structures (bcc, fcc, and diamond) and bonding types (metallic and covalent). This dataset comprises only the Ge configurations

ColabFit ID: mlearn_Ge_train_ZuoChenLiDengChenBehlerCsanyiShapeevThompsonWoodOng__DS_ot3m0rxle8fs_0
Name: mlearn_Ge_train
Authors: Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, Shyue Ping Ong
Elements: Ge
Number of Configurations: 228
Number of Elements: 1
Number of Atoms: 14,072
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/materialsvirtuallab/mlearn/tree/master/data
https://doi.org/10.1021/acs.jpca.9b08723

Download Dataset XYZ file Description: A comprehensive DFT data set was generated for six elements - Li, Mo, Ni, Cu, Si, and Ge. These elements were chosen to span a variety of chemistries (main group metal, transition metal, and semiconductor), crystal structures (bcc, fcc, and diamond) and bonding types (metallic and covalent). This dataset comprises only the Li configurations

ColabFit ID: mlearn_Li_test_ZuoChenLiDengChenBehlerCsanyiShapeevThompsonWoodOng__DS_jf20mvwao5xi_0
Name: mlearn_Li_test
Authors: Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, Shyue Ping Ong
Elements: Li
Number of Configurations: 29
Number of Elements: 1
Number of Atoms: 1,320
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/materialsvirtuallab/mlearn
https://doi.org/10.1021/acs.jpca.9b08723

Download Dataset XYZ file Description: A comprehensive DFT data set was generated for six elements - Li, Mo, Ni, Cu, Si, and Ge. These elements were chosen to span a variety of chemistries (main group metal, transition metal, and semiconductor), crystal structures (bcc, fcc, and diamond) and bonding types (metallic and covalent). This dataset comprises only the Li configurations

ColabFit ID: mlearn_Li_train_ZuoChenLiDengChenBehlerCsanyiShapeevThompsonWoodOng__DS_h7zao9ya9sd8_0
Name: mlearn_Li_train
Authors: Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, Shyue Ping Ong
Elements: Li
Number of Configurations: 241
Number of Elements: 1
Number of Atoms: 11,576
Property Types: potential-energy, atomic-forces, cauchy-stress

Links:
https://github.com/materialsvirtuallab/mlearn/tree/master/data
https://doi.org/10.1021/acs.jpca.9b08723