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The ColabFit Exchange: Data for Advanced Materials Science |
Welcome to the ColabFit Exchange! This is the world’s largest hub for the discovery, exploration and submission of datasets for the development of machine learning interatomic potentials (MLIPs) for materials science and chemistry. ColabFit datasets are carefully vetted and cleaned, and made available in a variety of standard formats including LMDB, Parquet and xyz. Content on the ColabFit Exchange is open source and freely available.
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Results: 421
23-Single-Element-DNPs_RSCDD_2023-Ag
Description :
Configurations of Ag from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Ag
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 3,795
Num. Atoms : 104,827
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Al
Description :
Configurations of Al from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Al
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 2,572
Num. Atoms : 88,139
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Au
Description :
Configurations of Au from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Au
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 3,601
Num. Atoms : 89,366
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Co
Description :
Configurations of Co from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Co
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 3,356
Num. Atoms : 67,320
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Cu
Description :
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Cu
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 3,366
Num. Atoms : 96,568
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Ge
Description :
Configurations of Ge from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Ge
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 2,895
Num. Atoms : 195,270
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-I
Description :
Configurations of I from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpo...
Authors :
Elements :
I
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 4,532
Num. Atoms : 115,902
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Kr
Description :
Configurations of Kr from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Kr
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 2,975
Num. Atoms : 97,920
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Li
Description :
Configurations of Li from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Li
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 2,536
Num. Atoms : 93,724
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Mg
Description :
Configurations of Mg from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Mg
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 3,004
Num. Atoms : 58,567
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Mo
Description :
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Mo
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 3,718
Num. Atoms : 66,612
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Nb
Description :
Configurations of Nb from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Nb
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 3,246
Num. Atoms : 56,191
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Ni
Description :
Configurations of Ni from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Ni
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 3,817
Num. Atoms : 75,534
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Os
Description :
Configurations of Os from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Os
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 4,779
Num. Atoms : 117,968
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Pb
Description :
Configurations of Pb from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Pb
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 5,350
Num. Atoms : 119,252
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Pd
Description :
Configurations of Pd from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Pd
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 3,478
Num. Atoms : 140,196
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Pt
Description :
Configurations of Pt from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Pt
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 2,609
Num. Atoms : 62,152
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Re
Description :
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Re
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 5,029
Num. Atoms : 101,248
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Sb
Description :
Configurations of Sb from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Sb
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 5,529
Num. Atoms : 122,289
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Sr
Description :
Configurations of Sr from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Sr
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 3,155
Num. Atoms : 49,426
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Ti
Description :
Configurations of Ti from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Ti
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 5,665
Num. Atoms : 153,659
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Zn
Description :
Configurations of Zn from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Zn
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 4,052
Num. Atoms : 107,039
Num. Elements : 1
23-Single-Element-DNPs_RSCDD_2023-Zr
Description :
Configurations of Zr from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purp...
Authors :
Elements :
Zr
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 4,730
Num. Atoms : 81,165
Num. Elements : 1
23-Single-Element-DNPs_all_trajectories
Description :
The full trajectories from the VASP runs used to generate the 23-Single-Element-DNPs training sets. Configuration sets are available for each element.
Authors :
Elements :
Ag, Al, Au, Co, Cu, Ge, I, Kr, Li, Mg, Mo, Nb, Ni, Os, P...
Source Data :
https://github.com/saidigroup/23-Single-Element-DNPs
Source Pub. :
https://doi.org/10.1039/D3DD00046J
Num. Configurations : 108,644
Num. Atoms : 2,352,424
Num. Elements : 23
3BPA_isolated_atoms
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...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1021/acs.jctc.1c00647
Source Pub. :
https://doi.org/10.1021/acs.jctc.1c00647
Num. Configurations : 4
Num. Atoms : 4
Num. Elements : 4
3BPA_test_1200K
Description :
Test configurations with MD simulations performed at 1200K from 3BPA, used to showcase the performance of linear atomic cluster expansion (ACE) force ...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1021/acs.jctc.1c00647
Source Pub. :
https://doi.org/10.1021/acs.jctc.1c00647
Num. Configurations : 2,139
Num. Atoms : 57,753
Num. Elements : 4
3BPA_test_300K
Description :
Test configurations with MD simulations performed at 300K from 3BPA, used to showcase the performance of linear atomic cluster expansion (ACE) force f...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1021/acs.jctc.1c00647
Source Pub. :
https://doi.org/10.1021/acs.jctc.1c00647
Num. Configurations : 1,669
Num. Atoms : 45,063
Num. Elements : 4
3BPA_test_600K
Description :
Test configurations with MD simulations performed at 600K from 3BPA, used to showcase the performance of linear atomic cluster expansion (ACE) force f...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1021/acs.jctc.1c00647
Source Pub. :
https://doi.org/10.1021/acs.jctc.1c00647
Num. Configurations : 2,138
Num. Atoms : 57,726
Num. Elements : 4
3BPA_test_dih_beta120
Description :
Test configurations with fixed value for dihedral beta in alpha-gamma plane of 120 degreesfrom 3BPA dataset. Used to showcase the performance of line...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1021/acs.jctc.1c00647
Source Pub. :
https://doi.org/10.1021/acs.jctc.1c00647
Num. Configurations : 2,347
Num. Atoms : 63,369
Num. Elements : 4
3BPA_test_dih_beta150
Description :
Test configurations with fixed value for dihedral beta in alpha-gamma plane of 150 degreesfrom 3BPA dataset. Used to showcase the performance of line...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1021/acs.jctc.1c00647
Source Pub. :
https://doi.org/10.1021/acs.jctc.1c00647
Num. Configurations : 2,350
Num. Atoms : 63,450
Num. Elements : 4
3BPA_test_dih_beta180
Description :
Test configurations with fixed value for dihedral beta in alpha-gamma plane of 180 degreesfrom 3BPA dataset. Used to showcase the performance of line...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1021/acs.jctc.1c00647
Source Pub. :
https://doi.org/10.1021/acs.jctc.1c00647
Num. Configurations : 2,350
Num. Atoms : 63,450
Num. Elements : 4
3BPA_train_300K
Description :
Training configurations with MD simulations performed at 300K from 3BPA, used to showcase the performance of linear atomic cluster expansion (ACE) for...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1021/acs.jctc.1c00647
Source Pub. :
https://doi.org/10.1021/acs.jctc.1c00647
Num. Configurations : 500
Num. Atoms : 13,500
Num. Elements : 4
3BPA_train_mixed
Description :
Training configurations with MD simulation performed at 300K, 600K and 1200K from 3BPA dataset, used to showcase the performance of linear atomic clus...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1021/acs.jctc.1c00647
Source Pub. :
https://doi.org/10.1021/acs.jctc.1c00647
Num. Configurations : 500
Num. Atoms : 13,500
Num. Elements : 4
ABC2D6-16_PRL_2018
Description :
Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpas...
Authors :
Elements :
Al, Ar, As, B, Ba, Be, Bi, Br, C, Ca, Cl, Cs, F, Ga, Ge,...
Source Data :
https://qmml.org/datasets.html
Source Pub. :
https://doi.org/10.1103/PhysRevLett.117.135502
Num. Configurations : 21,882
Num. Atoms : 218,820
Num. Elements : 39
AENET_amorphous_LiSi_JCP2021
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 f...
Authors :
Elements :
Li, Si
Source Data :
https://doi.org/10.24435/materialscloud:dx-ct
Source Pub. :
http://doi.org/10.1063/5.0063880
Num. Configurations : 44,652
Num. Atoms : 5,741,142
Num. Elements : 2
AENET_liquid_water_dataset_JCP2021
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 det...
Authors :
Elements :
H, O
Source Data :
https://doi.org/10.24435/materialscloud:dx-ct
Source Pub. :
http://doi.org/10.1063/5.0063880
Num. Configurations : 9,189
Num. Atoms : 1,788,288
Num. Elements : 2
AFF_JCP_2022
Description :
Approximately 145,000 configurations of alkane, aspirin, alpha-glucose and uracil, partly taken from the MD-17 dataset, used in training an 'Atomic Ne...
Authors :
Elements :
C, H, N, O
Source Pub. :
https://doi.org/10.1063/5.0088017
Num. Configurations : 143,770
Num. Atoms : 1,911,240
Num. Elements : 4
ANI-1
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 da...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.6084/m9.figshare.c.3846712.v1
Source Pub. :
https://doi.org/10.1038/sdata.2017.193
Num. Configurations : 24,416,306
Num. Atoms : 392,606,016
Num. Elements : 4
ANI-1x
Description :
ANI-1x contains DFT calculations for approximately 5 million molecular conformations. From an initial training set, an active learning method was used...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.6084/m9.figshare.c.4712477.v1
Source Pub. :
https://doi.org/10.1038/s41597-020-0473-z
Num. Configurations : 4,956,005
Num. Atoms : 75,700,481
Num. Elements : 4
ANI-2x-B973c-def2mTZVP
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...
Authors :
Elements :
C, Cl, F, H, N, O, S
Source Data :
https://doi.org/10.5281/zenodo.10108942
Source Pub. :
https://doi.org/10.1021/acs.jctc.0c00121
Num. Configurations : 9,643,594
Num. Atoms : 146,656,635
Num. Elements : 7
ANI-2x-wB97MD3BJ-def2TZVPP
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 contai...
Authors :
Elements :
C, Cl, F, H, N, O, S
Source Data :
https://doi.org/10.5281/zenodo.10108942
Source Pub. :
https://doi.org/10.1021/acs.jctc.0c00121
Num. Configurations : 9,650,572
Num. Atoms : 146,715,621
Num. Elements : 7
ANI-2x-wB97MV-def2TZVPP
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 containin...
Authors :
Elements :
C, Cl, F, H, N, O, S
Source Data :
https://doi.org/10.5281/zenodo.10108942
Source Pub. :
https://doi.org/10.1021/acs.jctc.0c00121
Num. Configurations : 9,650,572
Num. Atoms : 146,715,621
Num. Elements : 7
ANI-2x-wB97X-631Gd
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, ...
Authors :
Elements :
C, Cl, F, H, N, O, S
Source Data :
https://doi.org/10.5281/zenodo.10108942
Source Pub. :
https://doi.org/10.1021/acs.jctc.0c00121
Num. Configurations : 9,651,712
Num. Atoms : 146,736,809
Num. Elements : 7
ANI-Al_NC2021-test
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 a...
Authors :
Elements :
Al
Source Data :
https://github.com/atomistic-ml/ani-al
Source Pub. :
https://doi.org/10.1038/s41467-021-21376-0
Num. Configurations : 2,872
Num. Atoms : 371,645
Num. Elements : 1
ANI-Al_NC2021-train
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 ...
Authors :
Elements :
Al
Source Data :
https://github.com/atomistic-ml/ani-al
Source Pub. :
https://doi.org/10.1038/s41467-021-21376-0
Num. Configurations : 2,864
Num. Atoms : 375,121
Num. Elements : 1
Ag-PBE_MSMSE_2021
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.
Authors :
Elements :
Ag
Source Pub. :
https://doi.org/10.48550/arXiv.2108.06232
Num. Configurations : 7,608
Num. Atoms : 152,318
Num. Elements : 1
AgAu-nanoalloy_MSMSE_2021
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.
Authors :
Elements :
Ag, Au
Source Pub. :
https://doi.org/10.48550/arXiv.2108.06232
Num. Configurations : 51,771
Num. Atoms : 1,188,220
Num. Elements : 2
AgPd_NPJ_2021
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 wa...
Authors :
Elements :
Ag, Pd
Source Data :
https://github.com/msg-byu/agpd
Source Pub. :
https://doi.org/10.1038/s41524-020-00477-2
Num. Configurations : 1,691
Num. Atoms : 14,180
Num. Elements : 2
AlNiCu_AIP_2020
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 ...
Authors :
Elements :
Al, Cu, Ni
Source Pub. :
https://doi.org/10.1063/5.0016005
Num. Configurations : 1,017
Num. Atoms : 4,650
Num. Elements : 3
AlNiTi_CMS_2019
Description :
This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) struc...
Authors :
Elements :
Al, Ni, Ti
Source Data :
https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data
Source Pub. :
https://doi.org/10.1016/j.commatsci.2018.09.031
Num. Configurations : 2,684
Num. Atoms : 25,067
Num. Elements : 3
Al_Cu_Mg_GSFE_JMPS2019
Description :
Dataset from "Stress-dependence of generalized stacking fault energies":DFT calculations of generalized stacking fault energies (GSFE) for Al, Cu, and...
Authors :
Elements :
Al, Cu, Mg
Source Data :
https://doi.org/10.24435/materialscloud:2019.0089/v1
Source Pub. :
https://doi.org/10.1016/j.jmps.2018.09.007
Num. Configurations : 273
Num. Atoms : 3,264
Num. Elements : 3
Alexandria_geometry_optimization_paths_PBE_1D
Description :
The Alexandria Materials Database contains theoretical crystal structures in 1D, 2D and 3D discovered by machine learning approaches using DFT with PB...
Authors :
Elements :
Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl, Co...
Source Data :
https://alexandria.icams.rub.de/
Source Pub. :
https://doi.org/10.1002/adma.202210788
Num. Configurations : 614,833
Num. Atoms : 6,062,475
Num. Elements : 74
Alexandria_geometry_optimization_paths_PBE_2D
Description :
The Alexandria Materials Database contains theoretical crystal structures in 1D, 2D and 3D discovered by machine learning approaches using DFT with PB...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://alexandria.icams.rub.de/
Source Pub. :
https://doi.org/10.1002/adma.202210788
Num. Configurations : 11,742,482
Num. Atoms : 118,265,549
Num. Elements : 84
Alexandria_geometry_optimization_paths_PBE_3D
Description :
The Alexandria Materials Database contains theoretical crystal structures in 1D, 2D and 3D discovered by machine learning approaches using DFT with PB...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://alexandria.icams.rub.de/
Source Pub. :
https://doi.org/10.1002/adma.202210788
Num. Configurations : 106,825,218
Num. Atoms : 1,313,552,132
Num. Elements : 89
Au-PBE_MSMSE_2021
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.
Authors :
Elements :
Au
Source Pub. :
https://doi.org/10.48550/arXiv.2108.06232
Num. Configurations : 19,434
Num. Atoms : 310,792
Num. Elements : 1
BA10-18
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 include...
Authors :
Elements :
Ag, Al, Co, Cu, Fe, Mg, Nb, Ni, Ti, V
Source Data :
https://qmml.org/datasets.html
Source Pub. :
https://doi.org/10.1038/s41524-019-0189-9
Num. Configurations : 15,920
Num. Atoms : 116,380
Num. Elements : 10
BOTnet_ACAC_2022_Dihedral_scan
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 ...
Authors :
Elements :
C, H, O
Source Data :
https://github.com/davkovacs/BOTNet-datasets
Source Pub. :
https://doi.org/10.48550/arXiv.2205.06643
Num. Configurations : 45
Num. Atoms : 675
Num. Elements : 3
BOTnet_ACAC_2022_H_transfer
Description :
NEB path of proton transfer reaction between the two forms of acetylacetone. Acetylacetone dataset generated from a long molecular dynamics simulation...
Authors :
Elements :
C, H, O
Source Data :
https://github.com/davkovacs/BOTNet-datasets
Source Pub. :
https://doi.org/10.48550/arXiv.2205.06643
Num. Configurations : 15
Num. Atoms : 225
Num. Elements : 3
BOTnet_ACAC_2022_isolated
Description :
Energies of the isolated atoms evalauted at the reference DFT settings. Acetylacetone dataset generated from a long molecular dynamics simulation at 3...
Authors :
Elements :
C, H, O
Source Data :
https://github.com/davkovacs/BOTNet-datasets
Source Pub. :
https://doi.org/10.48550/arXiv.2205.06643
Num. Configurations : 3
Num. Atoms : 3
Num. Elements : 3
BOTnet_ACAC_2022_test_300K_MD
Description :
Test set of decorrelated geometries sampled from 300 K xTB MD. Acetylacetone dataset generated from a long molecular dynamics simulation at 300 K usin...
Authors :
Elements :
C, H, O
Source Data :
https://github.com/davkovacs/BOTNet-datasets
Source Pub. :
https://doi.org/10.48550/arXiv.2205.06643
Num. Configurations : 650
Num. Atoms : 9,750
Num. Elements : 3
BOTnet_ACAC_2022_test_600K_MD
Description :
Test set of decorrelated geometries sampled from 600 K xTB MD. Acetylacetone dataset generated from a long molecular dynamics simulation at 300 K usin...
Authors :
Elements :
C, H, O
Source Data :
https://github.com/davkovacs/BOTNet-datasets
Source Pub. :
https://doi.org/10.48550/arXiv.2205.06643
Num. Configurations : 650
Num. Atoms : 9,750
Num. Elements : 3
BOTnet_ACAC_2022_train_300K_MD
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 ...
Authors :
Elements :
C, H, O
Source Data :
https://github.com/davkovacs/BOTNet-datasets
Source Pub. :
https://doi.org/10.48550/arXiv.2205.06643
Num. Configurations : 500
Num. Atoms : 7,500
Num. Elements : 3
BOTnet_ACAC_2022_train_600K_MD
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 ...
Authors :
Elements :
C, H, O
Source Data :
https://github.com/davkovacs/BOTNet-datasets
Source Pub. :
https://doi.org/10.48550/arXiv.2205.06643
Num. Configurations : 500
Num. Atoms : 7,500
Num. Elements : 3
C7H10O2
Description :
6095 isomers of C7O2H10. Energetics were calculated at the G4MP2 level of theory.
Authors :
Elements :
C, H, O
Source Data :
https://doi.org/10.6084/m9.figshare.c.978904.v5
Source Pub. :
https://doi.org/10.1038/sdata.2014.22
Num. Configurations : 6,095
Num. Atoms : 115,805
Num. Elements : 3
CA-9_BB_training
Description :
Binning-binning configurations from CA-9 dataset used for training NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets ch...
Authors :
Elements :
C
Source Data :
https://doi.org/10.24435/materialscloud:6h-yj
Source Pub. :
https://doi.org/10.1016/j.cartre.2021.100027
Num. Configurations : 20,012
Num. Atoms : 1,054,055
Num. Elements : 1
CA-9_BB_validation
Description :
Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with cura...
Authors :
Elements :
C
Source Data :
https://doi.org/10.24435/materialscloud:6h-yj
Source Pub. :
https://doi.org/10.1016/j.cartre.2021.100027
Num. Configurations : 4,003
Num. Atoms : 233,034
Num. Elements : 1
CA-9_BR_training
Description :
Binning-random configurations from CA-9 dataset used for training NNP_BR potential. CA-9 consists of configurations of carbon with curated subsets cho...
Authors :
Elements :
C
Source Data :
https://doi.org/10.24435/materialscloud:6h-yj
Source Pub. :
https://doi.org/10.1016/j.cartre.2021.100027
Num. Configurations : 20,013
Num. Atoms : 1,072,779
Num. Elements : 1
CA-9_BR_validation
Description :
Binning-random configurations from CA-9 dataset used during validation step for NNP_BR potential. CA-9 consists of configurations of carbon with curat...
Authors :
Elements :
C
Source Data :
https://doi.org/10.24435/materialscloud:6h-yj
Source Pub. :
https://doi.org/10.1016/j.cartre.2021.100027
Num. Configurations : 4,002
Num. Atoms : 214,310
Num. Elements : 1
CA-9_RR_training
Description :
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chos...
Authors :
Elements :
C
Source Data :
https://doi.org/10.24435/materialscloud:6h-yj
Source Pub. :
https://doi.org/10.1016/j.cartre.2021.100027
Num. Configurations : 20,013
Num. Atoms : 1,100,042
Num. Elements : 1
CA-9_RR_validation
Description :
Random-random configurations from CA-9 dataset used during validation step for NNP_RR potential. CA-9 consists of configurations of carbon with curate...
Authors :
Elements :
C
Source Data :
https://doi.org/10.24435/materialscloud:6h-yj
Source Pub. :
https://doi.org/10.1016/j.cartre.2021.100027
Num. Configurations : 4,002
Num. Atoms : 218,184
Num. Elements : 1
CA-9_test
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 ...
Authors :
Elements :
C
Source Data :
https://doi.org/10.24435/materialscloud:6h-yj
Source Pub. :
https://doi.org/10.1016/j.cartre.2021.100027
Num. Configurations : 2,727
Num. Atoms : 206,302
Num. Elements : 1
CA-9_training
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 t...
Authors :
Elements :
C
Source Data :
https://doi.org/10.24435/materialscloud:6h-yj
Source Pub. :
https://doi.org/10.1016/j.cartre.2021.100027
Num. Configurations : 40,000
Num. Atoms : 2,195,399
Num. Elements : 1
CA-9_validation
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 ch...
Authors :
Elements :
C
Source Data :
https://doi.org/10.24435/materialscloud:6h-yj
Source Pub. :
https://doi.org/10.1016/j.cartre.2021.100027
Num. Configurations : 8,000
Num. Atoms : 436,601
Num. Elements : 1
CGM-MLP_natcomm2023_Cr-C_deposition
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 p...
Authors :
Elements :
C, Cr
Source Data :
https://github.com/sjtudizhang/CGM-MLP
Source Pub. :
https://doi.org/10.1038/s41467-023-44525-z
Num. Configurations : 1,192
Num. Atoms : 298,114
Num. Elements : 2
CGM-MLP_natcomm2023_Cu-C-O
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 du...
Authors :
Elements :
C, Cu, O
Source Data :
https://github.com/sjtudizhang/CGM-MLP
Source Pub. :
https://doi.org/10.1038/s41467-023-44525-z
Num. Configurations : 1,717
Num. Atoms : 387,151
Num. Elements : 3
CGM-MLP_natcomm2023_Cu-C-O_deposition
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 th...
Authors :
Elements :
C, Cu
Source Data :
https://github.com/sjtudizhang/CGM-MLP
Source Pub. :
https://doi.org/10.1038/s41467-023-44525-z
Num. Configurations : 1,694
Num. Atoms : 326,328
Num. Elements : 2
CGM-MLP_natcomm2023_Cu-C_deposition
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 p...
Authors :
Elements :
C, Cu
Source Data :
https://github.com/sjtudizhang/CGM-MLP
Source Pub. :
https://doi.org/10.1038/s41467-023-44525-z
Num. Configurations : 1,177
Num. Atoms : 204,591
Num. Elements : 2
CGM-MLP_natcomm2023_Cu-C_metal_surface
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 proces...
Authors :
Elements :
C, Cu
Source Data :
https://github.com/sjtudizhang/CGM-MLP
Source Pub. :
https://doi.org/10.1038/s41467-023-44525-z
Num. Configurations : 520
Num. Atoms : 122,294
Num. Elements : 2
CGM-MLP_natcomm2023_GAP_20
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 learni...
Authors :
Elements :
C, Cu
Source Data :
https://github.com/sjtudizhang/CGM-MLP
Source Pub. :
https://doi.org/10.1038/s41467-023-44525-z
Num. Configurations : 6,178
Num. Atoms : 400,485
Num. Elements : 2
CGM-MLP_natcomm2023_Ti-C_deposition
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 p...
Authors :
Elements :
C, Ti
Source Data :
https://github.com/sjtudizhang/CGM-MLP
Source Pub. :
https://doi.org/10.1038/s41467-023-44525-z
Num. Configurations : 1,309
Num. Atoms : 259,636
Num. Elements : 2
CGM-MLP_natcomm2023_screening_amorphous_carbon_test
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 ...
Authors :
Elements :
C
Source Data :
https://github.com/sjtudizhang/CGM-MLP
Source Pub. :
https://doi.org/10.1038/s41467-023-44525-z
Num. Configurations : 494
Num. Atoms : 32,279
Num. Elements : 1
CGM-MLP_natcomm2023_screening_amorphous_carbon_train
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 pro...
Authors :
Elements :
C
Source Data :
https://github.com/sjtudizhang/CGM-MLP
Source Pub. :
https://doi.org/10.1038/s41467-023-44525-z
Num. Configurations : 2,559
Num. Atoms : 168,191
Num. Elements : 1
CGM-MLP_natcomm2023_screening_carbon-cluster@Cu_test
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 t...
Authors :
Elements :
C, Cu
Source Data :
https://github.com/sjtudizhang/CGM-MLP
Source Pub. :
https://doi.org/10.1038/s41467-023-44525-z
Num. Configurations : 193
Num. Atoms : 38,004
Num. Elements : 2
CGM-MLP_natcomm2023_screening_carbon-cluster@Cu_train
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 ...
Authors :
Elements :
C, Cu
Source Data :
https://github.com/sjtudizhang/CGM-MLP
Source Pub. :
https://doi.org/10.1038/s41467-023-44525-z
Num. Configurations : 588
Num. Atoms : 115,460
Num. Elements : 2
CGM-MLP_natcomm2023_screening_deposited-carbon@Cu_test
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...
Authors :
Elements :
C, Cu
Source Data :
https://github.com/sjtudizhang/CGM-MLP
Source Pub. :
https://doi.org/10.1038/s41467-023-44525-z
Num. Configurations : 469
Num. Atoms : 156,312
Num. Elements : 2
CGM-MLP_natcomm2023_screening_deposited-carbon@Cu_train
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 t...
Authors :
Elements :
C, Cu
Source Data :
https://github.com/sjtudizhang/CGM-MLP
Source Pub. :
https://doi.org/10.1038/s41467-023-44525-z
Num. Configurations : 1,091
Num. Atoms : 362,898
Num. Elements : 2
CGM-MLP_natcomm2023_screening_graphite_train
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 ...
Authors :
Elements :
C
Source Data :
https://github.com/sjtudizhang/CGM-MLP
Source Pub. :
https://doi.org/10.1038/s41467-023-44525-z
Num. Configurations : 41
Num. Atoms : 1,968
Num. Elements : 1
CHON_JCP_2020
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,...
Authors :
Elements :
C, H, N, O
Source Data :
https://github.com/DescriptorZoo/sensitivity-dimensionality-results/tree/master/datasets
Source Pub. :
https://doi.org/10.1063/5.0016005
Num. Configurations : 5,216
Num. Atoms : 96,736
Num. Elements : 4
COHInPt_schaaf_2023
Description :
Training and simulation data from machine learning force field model applied to steps of the hydrogenation of carbon dioxide to methanol over an indiu...
Authors :
Elements :
C, H, In, O, Pt
Source Data :
https://doi.org/10.5281/zenodo.8268726
Source Pub. :
https://doi.org/10.48550/arXiv.2301.09931
Num. Configurations : 1,994
Num. Atoms : 163,746
Num. Elements : 5
COLL_test
Description :
Test set from COLL. Consists of configurations taken from molecular collisions of different small organic molecules. Energies and forces for 140,000 r...
Authors :
Elements :
C, H, O
Source Data :
https://doi.org/10.6084/m9.figshare.13289165.v1
Source Pub. :
https://doi.org/10.48550/arXiv.2011.14115
Num. Configurations : 9,480
Num. Atoms : 97,886
Num. Elements : 3
COLL_train
Description :
Training set from COLL. Consists of configurations taken from molecular collisions of different small organic molecules. Energies and forces for 140,0...
Authors :
Elements :
C, H, O
Source Data :
https://doi.org/10.6084/m9.figshare.13289165.v1
Source Pub. :
https://doi.org/10.48550/arXiv.2011.14115
Num. Configurations : 120,000
Num. Atoms : 1,225,350
Num. Elements : 3
COLL_validation
Description :
Validation set from COLL. Consists of configurations taken from molecular collisions of different small organic molecules. Energies and forces for 140...
Authors :
Elements :
C, H, O
Source Data :
https://doi.org/10.6084/m9.figshare.13289165.v1
Source Pub. :
https://doi.org/10.48550/arXiv.2011.14115
Num. Configurations : 10,000
Num. Atoms : 101,847
Num. Elements : 3
COMP6v2-B973c-def2mTZVP
Description :
COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) ...
Authors :
Elements :
C, Cl, F, H, N, O, S
Source Data :
https://doi.org/10.5281/zenodo.10126157
Source Pub. :
https://doi.org/10.1021/acs.jctc.0c00121
Num. Configurations : 156,330
Num. Atoms : 3,786,071
Num. Elements : 7
COMP6v2-wB97MD3BJ-def2TZVPP
Description :
COMP6v2-wB97MD3BJ-def2TZVPP is the portion of COMP6v2 calculated at the wB97MD3BJ/def2TZVPP level of theory. COmprehensive Machine-learning Potential ...
Authors :
Elements :
C, Cl, F, H, N, O, S
Source Data :
https://doi.org/10.5281/zenodo.10126157
Source Pub. :
https://doi.org/10.1021/acs.jctc.0c00121
Num. Configurations : 156,353
Num. Atoms : 3,787,055
Num. Elements : 7
COMP6v2-wB97MV-def2TZVPP
Description :
COMP6v2-wB97MV-def2TZVPP is the portion of COMP6v2 calculated at the wB97MV/def2TZVPP level of theory. COmprehensive Machine-learning Potential (COMP6...
Authors :
Elements :
C, Cl, F, H, N, O, S
Source Data :
https://doi.org/10.5281/zenodo.10126157
Source Pub. :
https://doi.org/10.1021/acs.jctc.0c00121
Num. Configurations : 156,369
Num. Atoms : 3,787,406
Num. Elements : 7
COMP6v2-wB97X-631Gd
Description :
COMP6v2-wB97X-631Gd is the portion of COMP6v2 calculated at the wB97X/631Gd level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmar...
Authors :
Elements :
C, Cl, F, H, N, O, S
Source Data :
https://doi.org/10.5281/zenodo.10126157
Source Pub. :
https://doi.org/10.1021/acs.jctc.0c00121
Num. Configurations : 157,728
Num. Atoms : 3,897,978
Num. Elements : 7
C_Gardner_2022
Description :
Approximately 115,000 configurations of carbon with 200 atoms, with simulated melt, quench, reheat, then annealing at the noted temperature. Includes ...
Authors :
Elements :
C
Source Data :
https://github.com/jla-gardner/carbon-data
Source Pub. :
https://doi.org/10.48550/arXiv.2211.16443
Num. Configurations : 115,206
Num. Atoms : 23,041,200
Num. Elements : 1
C_NPJ2020
Description :
The dataset consists of energies and forces for monolayer graphene, bilayer graphene, graphite, and diamond in various states, including strained stat...
Authors :
Elements :
C
Source Data :
https://doi.org/10.6084/m9.figshare.12649811.v1
Source Pub. :
https://doi.org/10.1038/s41524-020-00390-8
Num. Configurations : 4,776
Num. Atoms : 228,852
Num. Elements : 1
Carbon_GAP_JCP_2020
Description :
GAP-20 describes the properties of the bulk crystalline and amorphous phases, crystal surfaces, and defect structures with an accuracy approaching tha...
Authors :
Elements :
C
Source Data :
https://www.repository.cam.ac.uk/handle/1810/307452
Source Pub. :
https://doi.org/10.1063/5.0005084
Num. Configurations : 17,227
Num. Atoms : 1,312,457
Num. Elements : 1
Carbon_GAP_JCP_2020_train
Description :
Training data generated for GAP-20. GAP-20 describes the properties of the bulk crystalline and amorphous phases, crystal surfaces, and defect structu...
Authors :
Elements :
C
Source Data :
https://www.repository.cam.ac.uk/handle/1810/307452
Source Pub. :
https://doi.org/10.1063/5.0005084
Num. Configurations : 6,088
Num. Atoms : 400,275
Num. Elements : 1
Carbon_allotrope_multilayer_graphene_graphite_PRB2019
Description :
The dataset consists of energies and forces for pristine and defected monolayer graphene, bilayer graphene, and
graphite in various states. The confi...
Authors :
Elements :
C
Source Pub. :
https://doi.org/10.1103/PhysRevB.100.195419
Num. Configurations : 14,179
Num. Atoms : 656,204
Num. Elements : 1
Carolina_Materials
Description :
Carolina Materials contains structures used to train several machine learning models for the efficient generation of hypothetical inorganic materials....
Authors :
Elements :
Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Cl, Co, Cr...
Source Data :
https://zenodo.org/records/8381476
Source Pub. :
https://doi.org/10.1002/advs.202100566
Num. Configurations : 214,280
Num. Atoms : 3,168,502
Num. Elements : 64
Cationic_phenoxyimine_complexes_of_yttrium
Description :
This dataset contains DFT calculations that were carried out in conjunction with experimental investigation of a cationic phenoxyimine yttrium complex...
Authors :
Elements :
Al, B, C, F, H, N, O, Si, Y
Source Data :
https://doi.org/10.1021/acs.organomet.2c00238.s001
Source Pub. :
https://doi.org/10.1021/acs.organomet.2c00238
Num. Configurations : 109
Num. Atoms : 9,074
Num. Elements : 9
Chig-AIMD_random_test
Description :
Test configurations from the 'random' split of Chig-AIMD. This dataset covers the conformational space of chignolin with DFT-level precision. We seque...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1038/s41597-023-02465-9
Source Pub. :
https://doi.org/10.6084/m9.figshare.22786730.v4
Num. Configurations : 199,000
Num. Atoms : 33,034,000
Num. Elements : 4
Chig-AIMD_random_train
Description :
Training configurations from the 'random' split of Chig-AIMD. This dataset covers the conformational space of chignolin with DFT-level precision. We s...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1038/s41597-023-02465-9
Source Pub. :
https://doi.org/10.6084/m9.figshare.22786730.v4
Num. Configurations : 1,592,800
Num. Atoms : 264,404,800
Num. Elements : 4
Chig-AIMD_random_val
Description :
Validation configurations from the 'random' split of Chig-AIMD. This dataset covers the conformational space of chignolin with DFT-level precision. We...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1038/s41597-023-02465-9
Source Pub. :
https://doi.org/10.6084/m9.figshare.22786730.v4
Num. Configurations : 199,000
Num. Atoms : 33,034,000
Num. Elements : 4
Chig-AIMD_scaffold_test
Description :
Test configurations from the 'scaffold' split of Chig-AIMD. This dataset covers the conformational space of chignolin with DFT-level precision. We seq...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1038/s41597-023-02465-9
Source Pub. :
https://doi.org/10.6084/m9.figshare.22786730.v4
Num. Configurations : 199,000
Num. Atoms : 33,034,000
Num. Elements : 4
Chig-AIMD_scaffold_train
Description :
Training configurations from the 'scaffold' split of Chig-AIMD. This dataset covers the conformational space of chignolin with DFT-level precision. We...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1038/s41597-023-02465-9
Source Pub. :
https://doi.org/10.6084/m9.figshare.22786730.v4
Num. Configurations : 1,592,800
Num. Atoms : 264,404,800
Num. Elements : 4
Chig-AIMD_scaffold_val
Description :
Validation configurations from the 'scaffold' split of Chig-AIMD. This dataset covers the conformational space of chignolin with DFT-level precision. ...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1038/s41597-023-02465-9
Source Pub. :
https://doi.org/10.6084/m9.figshare.22786730.v4
Num. Configurations : 199,000
Num. Atoms : 33,034,000
Num. Elements : 4
Co-Co_coupling_at_liquid_water-Cu(100)_interfaces_JC2021
Description :
This dataset contains data from eight AIMD simulations run in VASP to study electrochemical *CO-*CO coupling -- coupling of two *CO molecules -- at th...
Authors :
Elements :
C, Cs, Cu, H, Li, O
Source Data :
https://doi.org/10.24435/materialscloud:p9-q7
Source Pub. :
https://doi.org/10.1016/j.jcat.2021.02.023
Num. Configurations : 1,671,203
Num. Atoms : 226,264,514
Num. Elements : 6
CoCrFeNiPd_MRL2020
Description :
The dataset for "Origin of high strength in the CoCrFeNiPd high-entropy alloy", containing DFT-calculated values of the high-entropy alloy CoCrFeNiPd,...
Authors :
Elements :
Co, Cr, Fe, Ni, Pd
Source Data :
https://doi.org/10.24435/materialscloud:2020.0045/v1
Source Pub. :
https://doi.org/10.24435/materialscloud:2020.0045/v1
Num. Configurations : 116
Num. Atoms : 8,552
Num. Elements : 5
CoNbV_CMS2019
Description :
This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) struc...
Authors :
Elements :
Co, Nb, V
Source Data :
https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data
Source Pub. :
https://doi.org/10.1016/j.commatsci.2018.09.031
Num. Configurations : 383
Num. Atoms : 2,812
Num. Elements : 3
Co_dimer_JPCA_2022
Description :
This dataset contains dimer molecules of Co(II) with potential energy calculations for structures with ferromagnetic and antiferromagnetic spin config...
Authors :
Elements :
C, Cl, Co, H, N, O, P, S
Source Data :
https://doi.org/10.24435/materialscloud:pe-zv
Source Pub. :
https://doi.org/10.1021/acs.jpca.1c08950
Num. Configurations : 2,162
Num. Atoms : 188,438
Num. Elements : 8
Co_dimer_JPCA_2022_train
Description :
Training data only from the Co_dimer_JPCA_2022 dataset. This dataset contains dimer molecules of Co(II) with potential energy calculations for structu...
Authors :
Elements :
C, Cl, Co, H, N, O, P, S
Source Data :
https://doi.org/10.24435/materialscloud:pe-zv
Source Pub. :
https://doi.org/10.1021/acs.jpca.1c08950
Num. Configurations : 1,798
Num. Atoms : 154,882
Num. Elements : 8
ComBat
Description :
DFT-optimized geometries and properties for Li-S electrolytes. These make up the Computational Database for Li-S Batteries (ComBat), calculated using ...
Authors :
Elements :
C, F, H, Li, N, O, P, S, Si
Source Data :
https://github.com/rashatwi/combat/
Source Pub. :
https://doi.org/10.1038/s41598-022-20009-w
Num. Configurations : 230
Num. Atoms : 5,662
Num. Elements : 9
CrCoNi_Cao_2022
Description :
Training dataset that captures chemical short-range order in equiatomic CrCoNi medium-entropy alloy published with our work Quantifying chemical short...
Authors :
Elements :
Co, Cr, Ni
Source Data :
https://github.com/yifan-henry-cao/MachineLearningPotential/blob/main/Training_datasets/Training_Cao_20220823.cfg
Source Pub. :
https://arxiv.org/abs/2311.01545
Num. Configurations : 1,257
Num. Atoms : 108,684
Num. Elements : 3
CuPd_CMS2019
Description :
This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) struc...
Authors :
Elements :
Cu, Pd
Source Data :
https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data
Source Pub. :
https://doi.org/10.1016/j.commatsci.2018.09.031
Num. Configurations : 522
Num. Atoms : 2,450
Num. Elements : 2
Cu_FHI-aims_NPJCM_2021
Description :
Approximately 46,000 configurations of copper, including small and bulk structures, surfaces, interfaces, point defects, and randomly modified variant...
Authors :
Elements :
Cu
Source Data :
https://doi.org/10.5281/zenodo.4734035
Source Pub. :
https://doi.org/10.1038/s41524-021-00559-9
Num. Configurations : 46,583
Num. Atoms : 308,679
Num. Elements : 1
DAS_MLIP_CoSb_MgSb
Description :
Approximately 850 configurations of CoSb3 and Mg3Sb2 generated using a dual adaptive sampling (DAS) method for use with machine learning of interatomi...
Authors :
Elements :
Mg, Sb
Source Data :
https://doi.org/10.1103/PhysRevB.104.094310
Source Pub. :
https://doi.org/10.1103/PhysRevB.104.094310
Num. Configurations : 846
Num. Atoms : 247,744
Num. Elements : 2
DFT_polymorphs_PNAS_2022_PBE0_MBD_benzene_test
Description :
Benzene test PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and g...
Authors :
Elements :
C, H
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 200
Num. Atoms : 5,760
Num. Elements : 2
DFT_polymorphs_PNAS_2022_PBE0_MBD_benzene_train
Description :
Benzene training PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, a...
Authors :
Elements :
C, H
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 1,800
Num. Atoms : 49,536
Num. Elements : 2
DFT_polymorphs_PNAS_2022_PBE0_MBD_benzene_validation
Description :
Benzene validation PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid,...
Authors :
Elements :
C, H
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 200
Num. Atoms : 6,072
Num. Elements : 2
DFT_polymorphs_PNAS_2022_PBE0_MBD_glycine_test
Description :
Glycine test PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and g...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 200
Num. Atoms : 6,880
Num. Elements : 4
DFT_polymorphs_PNAS_2022_PBE0_MBD_glycine_train
Description :
Glycine training PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, a...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 3,594
Num. Atoms : 109,940
Num. Elements : 4
DFT_polymorphs_PNAS_2022_PBE0_MBD_glycine_validation
Description :
Glycine validation PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid,...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 200
Num. Atoms : 7,120
Num. Elements : 4
DFT_polymorphs_PNAS_2022_PBE0_MBD_succinic_acid_test
Description :
Succinic acid test PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid,...
Authors :
Elements :
C, H, O
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 200
Num. Atoms : 5,600
Num. Elements : 3
DFT_polymorphs_PNAS_2022_PBE0_MBD_succinic_acid_train
Description :
Succinic acid training PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic a...
Authors :
Elements :
C, H, O
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 1,800
Num. Atoms : 50,400
Num. Elements : 3
DFT_polymorphs_PNAS_2022_PBE0_MBD_succinic_acid_validation
Description :
Succinic acid validation PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic...
Authors :
Elements :
C, H, O
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 200
Num. Atoms : 5,600
Num. Elements : 3
DFT_polymorphs_PNAS_2022_PBE_TS_benzene_test
Description :
Benzene test PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and gly...
Authors :
Elements :
C, H
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 1,000
Num. Atoms : 29,736
Num. Elements : 2
DFT_polymorphs_PNAS_2022_PBE_TS_benzene_train
Description :
Benzene training PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and...
Authors :
Elements :
C, H
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 55,000
Num. Atoms : 1,602,048
Num. Elements : 2
DFT_polymorphs_PNAS_2022_PBE_TS_benzene_validation
Description :
Benzene validation PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, a...
Authors :
Elements :
C, H
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 1,000
Num. Atoms : 29,712
Num. Elements : 2
DFT_polymorphs_PNAS_2022_PBE_TS_glycine_test
Description :
Glycine test PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and gly...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 500
Num. Atoms : 17,710
Num. Elements : 4
DFT_polymorphs_PNAS_2022_PBE_TS_glycine_train
Description :
Glycine training PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 29,070
Num. Atoms : 952,650
Num. Elements : 4
DFT_polymorphs_PNAS_2022_PBE_TS_glycine_validation
Description :
Glycine validation PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, a...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 500
Num. Atoms : 17,800
Num. Elements : 4
DFT_polymorphs_PNAS_2022_PBE_TS_succinic_acid_test
Description :
Succinic acid test PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, a...
Authors :
Elements :
C, H, O
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 500
Num. Atoms : 14,000
Num. Elements : 3
DFT_polymorphs_PNAS_2022_PBE_TS_succinic_acid_train
Description :
Succinic acid training PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic aci...
Authors :
Elements :
C, H, O
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 29,212
Num. Atoms : 817,936
Num. Elements : 3
DFT_polymorphs_PNAS_2022_PBE_TS_succinic_acid_validation
Description :
Succinic acid validation PBE-TS dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic a...
Authors :
Elements :
C, H, O
Source Data :
https://doi.org/10.24435/materialscloud:vp-jf
Source Pub. :
https://doi.org/10.1073/pnas.2111769119
Num. Configurations : 500
Num. Atoms : 14,000
Num. Elements : 3
DP-GEN_Cu
Description :
Approximately 15,000 configurations of copper used to demonstrate the DP-GEN data generator for PES machine learning models.
Authors :
Elements :
Cu
Source Pub. :
https://doi.org/10.1016/j.cpc.2020.107206
Num. Configurations : 15,286
Num. Atoms : 297,597
Num. Elements : 1
DeePMD_SE
Description :
127,000 configurations from a dataset used to benchmark and train a modified DeePMD model called DeepPot-SE, or Deep Potential - Smooth Edition
Authors :
Elements :
Al, C, Co, Cr, Cu, Fe, Ge, H, Mn, Mo, N, Ni, O, Pt, S, S...
Source Pub. :
https://doi.org/10.48550/arXiv.1805.09003
Num. Configurations : 127,112
Num. Atoms : 26,278,380
Num. Elements : 17
Fe_nanoparticles_PRB_2023
Description :
This iron nanoparticles database contains dimers; trimers; bcc, fcc, hexagonal close-packed (hcp), simple cubic, and diamond crystalline structures. A...
Authors :
Elements :
Fe
Source Data :
https://doi.org/10.5281/zenodo.7632315
Source Pub. :
https://doi.org/10.1103/PhysRevB.107.245421
Num. Configurations : 198
Num. Atoms : 20,097
Num. Elements : 1
FitSNAP_Fe_NPJ_2021
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 inter...
Authors :
Elements :
Fe
Source Data :
https://github.com/FitSNAP
Source Pub. :
https://doi.org/10.1038/s41524-021-00617-2
Num. Configurations : 2,517
Num. Atoms : 61,526
Num. Elements : 1
Forces_are_not_enough
Description :
Approximately 300,000 benchmarking configurations derived partly from the MD-17 and LiPS datasets, partly from original simulated water and alanine di...
Authors :
Elements :
C, H, Li, N, O, P, S
Source Data :
https://doi.org/10.5281/zenodo.7196767
Source Pub. :
https://doi.org/10.48550/arXiv.2210.07237
Num. Configurations : 295,001
Num. Atoms : 23,735,083
Num. Elements : 7
GDB_9_nature_2014
Description :
133,855 configurations of stable small organic molecules composed of CHONF. A subset of GDB-17, with calculations of energies, dipole moment, polariza...
Authors :
Elements :
C, F, H, N, O
Source Data :
https://doi.org/10.6084/m9.figshare.c.978904.v5
Source Pub. :
https://doi.org/10.1038/sdata.2014.22
Num. Configurations : 133,885
Num. Atoms : 2,407,753
Num. Elements : 5
GFN-xTB_JCIM_2021
Description :
10,000 configurations of organosilicon compounds with energies predicted by an improved GFN-xTB Hamiltonian parameterization, using revPBE.
Authors :
Elements :
Br, C, Cl, F, H, N, O, P, S, Si
Source Data :
https://doi.org/10.24435/materialscloud:14-4m
Source Pub. :
https://doi.org/10.1021/acs.jcim.1c01170
Num. Configurations : 157,367
Num. Atoms : 4,022,149
Num. Elements : 10
GST_GAP_22_extended
Description :
The extended training dataset for GST_GAP_22, calculated using the PBEsol functional. New configurations, simulated under external electric fields, we...
Authors :
Elements :
Ge, Sb, Te
Source Data :
https://doi.org/10.5281/zenodo.8208202
Source Pub. :
https://doi.org/10.1038/s41928-023-01030-x
Num. Configurations : 2,916
Num. Atoms : 399,247
Num. Elements : 3
GST_GAP_22_main
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 ...
Authors :
Elements :
Ge, Sb, Te
Source Data :
https://doi.org/10.5281/zenodo.8208202
Source Pub. :
https://doi.org/10.1038/s41928-023-01030-x
Num. Configurations : 2,692
Num. Atoms : 341,068
Num. Elements : 3
GST_GAP_22_refitted
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-...
Authors :
Elements :
Ge, Sb, Te
Source Data :
https://doi.org/10.5281/zenodo.8208202
Source Pub. :
https://doi.org/10.1038/s41928-023-01030-x
Num. Configurations : 2,692
Num. Atoms : 341,004
Num. Elements : 3
HDNNP_H2O
Description :
Approximately 28,000 configurations split into 4 datasets, each using a different functional, used in the training of a high-dimensional neural networ...
Authors :
Elements :
H, O
Source Data :
https://doi.org/10.5281/zenodo.2634097
Source Pub. :
https://doi.org/10.1073/pnas.1602375113
Num. Configurations : 28,678
Num. Atoms : 2,327,628
Num. Elements : 2
HEA25S_high_entropy_alloys
Description :
Dataset from "Surface segregation in high-entropy alloys from alchemical machine learning: dataset HEA25S". Includes 10000 bulk HEA structures (Datase...
Authors :
Elements :
Ag, Au, Co, Cr, Cu, Fe, Hf, Ir, Lu, Mn, Mo, Nb, Ni, Pd, ...
Source Data :
https://doi.org/10.24435/materialscloud:ps-20
Source Pub. :
http://doi.org/10.48550/arXiv.2310.07604
Num. Configurations : 15,004
Num. Atoms : 633,387
Num. Elements : 25
HEA25_high_entropy_transition-metal_alloys
Description :
Dataset from "Modeling high-entropy transition-metal alloys with alchemical compression". Includes 25,000 structures utilized for fitting the aforemen...
Authors :
Elements :
Ag, Au, Co, Cr, Cu, Fe, Hf, Ir, Lu, Mn, Mo, Nb, Ni, Pd, ...
Source Data :
https://doi.org/10.24435/materialscloud:73-yn
Source Pub. :
http://doi.org/10.48550/arXiv.2212.13254
Num. Configurations : 25,627
Num. Atoms : 1,063,680
Num. Elements : 25
HME21_test
Description :
The test set from HME21. The high-temperature multi-element 2021 (HME21) dataset comprises approximately 25,000 configurations, including 37 elements...
Authors :
Elements :
Ag, Al, Au, Ba, C, Ca, Cl, Co, Cr, Cu, F, Fe, H, In, Ir,...
Source Data :
https://doi.org/10.6084/m9.figshare.19658538.v2
Source Pub. :
https://doi.org/10.1038/s41467-022-30687-9
Num. Configurations : 2,495
Num. Atoms : 69,572
Num. Elements : 37
HME21_train
Description :
The training set from HME21. The high-temperature multi-element 2021 (HME21) dataset comprises approximately 25,000 configurations, including 37 elem...
Authors :
Elements :
Ag, Al, Au, Ba, C, Ca, Cl, Co, Cr, Cu, F, Fe, H, In, Ir,...
Source Data :
https://doi.org/10.6084/m9.figshare.19658538.v2
Source Pub. :
https://doi.org/10.1038/s41467-022-30687-9
Num. Configurations : 19,956
Num. Atoms : 555,050
Num. Elements : 37
HME21_validation
Description :
The validation set from HME21. The high-temperature multi-element 2021 (HME21) dataset comprises approximately 25,000 configurations, including 37 el...
Authors :
Elements :
Ag, Al, Au, Ba, C, Ca, Cl, Co, Cr, Cu, F, Fe, H, In, Ir,...
Source Data :
https://doi.org/10.6084/m9.figshare.19658538.v2
Source Pub. :
https://doi.org/10.1038/s41467-022-30687-9
Num. Configurations : 2,498
Num. Atoms : 69,420
Num. Elements : 37
HO_LiMoNiTi_NPJCM_2020_LiMoNiTi_train
Description :
Training configurations of Li8Mo2Ni7Ti7O32 from HO_LiMoNiTi_NPJCM_2020 used in the training of an ANN, whereby total energy is extrapolated by a Taylo...
Authors :
Elements :
Li, Mo, Ni, O, Ti
Source Data :
https://doi.org/10.24435/materialscloud:2020.0037/v1
Source Pub. :
https://doi.org/10.1038/s41524-020-0323-8
Num. Configurations : 824
Num. Atoms : 46,144
Num. Elements : 5
HO_LiMoNiTi_NPJCM_2020_LiMoNiTi_validation
Description :
Validation configurations of Li8Mo2Ni7Ti7O32 from HO_LiMoNiTi_NPJCM_2020 used in the training of an ANN, whereby total energy is extrapolated by a Tay...
Authors :
Elements :
Li, Mo, Ni, O, Ti
Source Data :
https://doi.org/10.24435/materialscloud:2020.0037/v1
Source Pub. :
https://doi.org/10.1038/s41524-020-0323-8
Num. Configurations : 1,792
Num. Atoms : 100,352
Num. Elements : 5
HO_LiMoNiTi_NPJCM_2020_bulk_water_train_test
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 ...
Authors :
Elements :
H, O
Source Data :
https://doi.org/10.24435/materialscloud:2020.0037/v1
Source Pub. :
https://doi.org/10.1038/s41524-020-0323-8
Num. Configurations : 700
Num. Atoms : 134,400
Num. Elements : 2
HO_LiMoNiTi_NPJCM_2020_bulk_water_validation
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 e...
Authors :
Elements :
H, O
Source Data :
https://doi.org/10.24435/materialscloud:2020.0037/v1
Source Pub. :
https://doi.org/10.1038/s41524-020-0323-8
Num. Configurations : 2,112
Num. Atoms : 405,504
Num. Elements : 2
HO_LiMoNiTi_NPJCM_2020_water_clusters
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 expansio...
Authors :
Elements :
H, O
Source Data :
https://doi.org/10.24435/materialscloud:2020.0037/v1
Source Pub. :
https://doi.org/10.1038/s41524-020-0323-8
Num. Configurations : 1,848
Num. Atoms : 33,264
Num. Elements : 2
HO_PNAS_2019
Description :
1590 configurations of H2O/water with total energy and forces calculated using a hybrid approach at DFT/revPBE0-D3 level of theory.
Authors :
Elements :
H, O
Source Data :
https://archive.materialscloud.org/record/2018.0020/v1
Source Pub. :
https://doi.org/10.1073/pnas.1815117116
Num. Configurations : 1,588
Num. Atoms : 304,896
Num. Elements : 2
HPt_NC_2022
Description :
A training dataset of 90,000 configurations with interaction properties between H2 and Pt(111) surfaces.
Authors :
Elements :
H, Pt
Source Data :
https://doi.org/10.24435/materialscloud:r0-84
Source Pub. :
https://doi.org/10.1038/s41467-022-32294-0
Num. Configurations : 90,740
Num. Atoms : 5,706,023
Num. Elements : 2
H_nature_2022
Description :
Over 300,000 configurations in an expanded dataset of 19 hydrogen combustion reaction channels. Intrinsic reaction coordinate calculations (IRC) are c...
Authors :
Elements :
H, O
Source Data :
https://doi.org/10.6084/m9.figshare.19601689.v3
Source Pub. :
https://doi.org/10.1038/s41597-022-01330-5
Num. Configurations : 350,121
Num. Atoms : 1,513,654
Num. Elements : 2
HfO2_DPGEN_PRB_2021
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,...
Authors :
Elements :
Hf, O
Source Pub. :
https://doi.org/10.1103/PhysRevB.103.024108
Num. Configurations : 28,564
Num. Atoms : 2,741,376
Num. Elements : 2
HfO2_NPJ_2020
Description :
6000 configurations of liquid and amorphous HfO2 generated for use with an active learning ML model.
Authors :
Elements :
Hf, O
Source Data :
https://github.com/argonne-lcf/active-learning-md
Source Pub. :
https://doi.org/10.1038/s41524-020-00367-7
Num. Configurations : 6,000
Num. Atoms : 576,000
Num. Elements : 2
Hydrogen-induced_insulating_state_SmNiO3
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....
Authors :
Elements :
H, Ni, O, Sm
Source Data :
https://doi.org/10.24435/materialscloud:4w-qm
Source Pub. :
https://doi.org/10.48550/arXiv.2210.07656
Num. Configurations : 3,318
Num. Atoms : 156,419
Num. Elements : 4
ISO17_NC_2017
Description :
129 molecules of composition C7O2H10 from the QM9 dataset with 5000 conformational geometries apiece. Molecular dynamics data was simulated using the ...
Authors :
Elements :
C, H, O
Source Data :
http://quantum-machine.org/datasets/
Num. Configurations : 640,855
Num. Atoms : 12,176,245
Num. Elements : 3
In2Se3_2D_DPGEN
Description :
Approximately 11,500 configurations of In2Se3, including monolayer (20-atom slab) and bulk (30-atom supercell) models.
Authors :
Elements :
In, Se
Source Pub. :
https://doi.org/10.1103/PhysRevB.104.174107
Num. Configurations : 11,523
Num. Atoms : 248,510
Num. Elements : 2
InP_JPCA2020
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). Inte...
Authors :
Elements :
In, P
Source Pub. :
https://doi.org/10.1021/acs.jpca.0c02450
Num. Configurations : 1,802
Num. Atoms : 106,761
Num. Elements : 2
JARVIS-Polymer-Genome
Description :
The JARVIS-Polymer-Genome dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contain...
Authors :
Elements :
Al, C, Ca, Cd, Cl, F, H, Hf, Mg, N, O, Pb, S, Sn, Ti, Zn...
Source Data :
https://ndownloader.figshare.com/files/26809907
Source Pub. :
https://doi.org/10.1038/sdata.2016.12
Num. Configurations : 1,073
Num. Atoms : 34,441
Num. Elements : 17
JARVIS-QM9-DGL
Description :
The JARVIS-QM9-DGL dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains confi...
Authors :
Elements :
C, F, H, N, O
Source Data :
https://ndownloader.figshare.com/files/28541196
Source Pub. :
https://doi.org/10.1038/sdata.2014.22
Num. Configurations : 130,831
Num. Atoms : 2,358,210
Num. Elements : 5
JARVIS_2DMatPedia
Description :
The JARVIS-2DMatPedia dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This subset contains con...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://ndownloader.figshare.com/files/26789006
Source Pub. :
https://doi.org/10.1038/s41597-019-0097-3
Num. Configurations : 6,351
Num. Atoms : 66,295
Num. Elements : 83
JARVIS_AGRA_CHO
Description :
The JARVIS_AGRA_CHO dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This dataset contains ...
Authors :
Elements :
C, Co, Cu, Fe, H, Mo, Ni, O
Source Data :
https://figshare.com/ndownloader/files/41923284
Source Pub. :
https://doi.org/10.1021/acscatal.2c03675
Num. Configurations : 216
Num. Atoms : 14,472
Num. Elements : 8
JARVIS_AGRA_CO
Description :
The JARVIS_AGRA_CO dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This dataset contains d...
Authors :
Elements :
C, Co, Cu, Fe, Mo, Ni, O
Source Data :
https://figshare.com/ndownloader/files/41923284
Source Pub. :
https://doi.org/10.1021/acscatal.2c03675
Num. Configurations : 194
Num. Atoms : 12,804
Num. Elements : 7
JARVIS_AGRA_COOH
Description :
The JARVIS_AGRA_COOH dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This dataset contains...
Authors :
Elements :
C, Co, Cu, Fe, H, Mo, Ni, O
Source Data :
https://figshare.com/ndownloader/files/41923284
Source Pub. :
https://doi.org/10.1021/acscatal.2c03675
Num. Configurations : 280
Num. Atoms : 19,040
Num. Elements : 8
JARVIS_AGRA_O
Description :
The JARVIS_AGRA_O dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This dataset contains da...
Authors :
Elements :
Ir, O, Pd, Pt, Rh, Ru
Source Data :
https://figshare.com/ndownloader/files/41923284
Source Pub. :
https://doi.org/10.1016/j.joule.2018.12.015
Num. Configurations : 1,000
Num. Atoms : 17,000
Num. Elements : 6
JARVIS_AGRA_OH
Description :
The JARVIS_AGRA_OH dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This dataset contains d...
Authors :
Elements :
H, Ir, O, Pd, Pt, Rh, Ru
Source Data :
https://figshare.com/ndownloader/files/41923284
Source Pub. :
https://doi.org/10.1016/j.joule.2018.12.015
Num. Configurations : 877
Num. Atoms : 15,786
Num. Elements : 7
JARVIS_ALIGNN_FF
Description :
The JARVIS_ALIGNN_FF dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset is a subset ...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://ndownloader.figshare.com/files/38522315
Source Pub. :
https://doi.org/10.1039/D2DD00096B
Num. Configurations : 306,411
Num. Atoms : 3,193,703
Num. Elements : 89
JARVIS_C2DB
Description :
The JARVIS-C2DB dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This subset contains configura...
Authors :
Elements :
Ag, Al, As, Au, B, Ba, Bi, Br, C, Ca, Cd, Cl, Co, Cr, Cs...
Source Data :
https://ndownloader.figshare.com/files/28682010
Source Pub. :
https://doi.org/10.1088/2053-1583/aacfc1
Num. Configurations : 3,520
Num. Atoms : 17,990
Num. Elements : 61
JARVIS_CFID_3D_8_18_2022
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 c...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://doi.org/10.6084/m9.figshare.6815699
Source Pub. :
https://doi.org/10.1038/s41524-020-00440-1
Num. Configurations : 55,631
Num. Atoms : 561,738
Num. Elements : 89
JARVIS_CFID_OQMD
Description :
The JARVIS_CFID_OQMD dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains con...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://ndownloader.figshare.com/files/24981170
Source Pub. :
https://doi.org/10.1038/npjcompumats.2015.10
Num. Configurations : 459,991
Num. Atoms : 2,366,255
Num. Elements : 89
JARVIS_DFT_2D_3_12_2021
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 ...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://ndownloader.figshare.com/files/26808917
Source Pub. :
https://doi.org/10.1038/s41524-020-00440-1
Num. Configurations : 1,074
Num. Atoms : 7,903
Num. Elements : 81
JARVIS_DFT_3D_12_12_2022
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...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://doi.org/10.6084/m9.figshare.6815699
Source Pub. :
https://doi.org/10.1038/s41524-020-00440-1
Num. Configurations : 75,909
Num. Atoms : 785,250
Num. Elements : 89
JARVIS_DFT_3D_8_18_2021
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 co...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://doi.org/10.6084/m9.figshare.6815699
Source Pub. :
https://doi.org/10.1038/s41524-020-00440-1
Num. Configurations : 55,631
Num. Atoms : 561,741
Num. Elements : 89
JARVIS_EPC_2D
Description :
The JARVIS_EPC_2D dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset contains con...
Authors :
Elements :
Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cl, Co, Cr, Cu...
Source Data :
https://figshare.com/ndownloader/files/38950433
Source Pub. :
https://doi.org/10.1021/acs.nanolett.2c04420
Num. Configurations : 161
Num. Atoms : 788
Num. Elements : 55
JARVIS_MEGNet
Description :
The JARVIS-MEGNet dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This subset contains configu...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://ndownloader.figshare.com/files/26724977
Source Pub. :
https://doi.org/10.1021/acs.chemmater.9b01294
Num. Configurations : 69,234
Num. Atoms : 2,070,948
Num. Elements : 89
JARVIS_MEGNet2
Description :
The JARVIS-MEGNet2 dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This subset contains 133K m...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://ndownloader.figshare.com/files/28332741
Source Pub. :
https://doi.org/10.1021/acs.chemmater.9b01294
Num. Configurations : 133,420
Num. Atoms : 3,880,497
Num. Elements : 89
JARVIS_Materials_Project_2020
Description :
The JARVIS_Materials_Project_2020 dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This sub...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://ndownloader.figshare.com/files/26791259
Source Pub. :
https://doi.org/10.1063/1.4812323
Num. Configurations : 126,335
Num. Atoms : 3,725,727
Num. Elements : 89
JARVIS_Materials_Project_84K
Description :
The JARVIS_Materials_Project_84K dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subs...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://ndownloader.figshare.com/files/24979850
Source Pub. :
https://doi.org/10.1063/1.4812323
Num. Configurations : 83,426
Num. Atoms : 2,339,932
Num. Elements : 89
JARVIS_OMDB
Description :
The JARVIS_OMDB dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configur...
Authors :
Elements :
Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Cl, Co, Cr...
Source Data :
https://ndownloader.figshare.com/files/28501761
Source Pub. :
https://doi.org/10.1002/qute.201900023
Num. Configurations : 12,497
Num. Atoms : 1,061,362
Num. Elements : 65
JARVIS_OQMD_no_CFID
Description :
The JARVIS_OQMD_no_CFID dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains ...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://ndownloader.figshare.com/files/26790182
Source Pub. :
https://doi.org/10.1038/npjcompumats.2015.10
Num. Configurations : 811,782
Num. Atoms : 5,017,701
Num. Elements : 89
JARVIS_Open_Catalyst_100K
Description :
The JARVIS_Open_Catalyst_100K dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset ...
Authors :
Elements :
Ag, Al, As, Au, B, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe...
Source Data :
https://figshare.com/ndownloader/files/40902845
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 124,943
Num. Atoms : 9,720,870
Num. Elements : 56
JARVIS_Open_Catalyst_10K
Description :
The JARVIS_Open_Catalyst_10K dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset c...
Authors :
Elements :
Ag, Al, As, Au, B, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe...
Source Data :
https://figshare.com/ndownloader/files/40566122
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 34,943
Num. Atoms : 2,720,316
Num. Elements : 56
JARVIS_Open_Catalyst_All
Description :
The JARVIS_Open_Catalyst_All dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset c...
Authors :
Elements :
Ag, Al, As, Au, B, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe...
Source Data :
https://figshare.com/ndownloader/files/40902845
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 485,269
Num. Atoms : 37,728,919
Num. Elements : 56
JARVIS_QE_TB
Description :
The QE-TB dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset contains configurati...
Authors :
Elements :
Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Cl, Co, Cr...
Source Data :
https://ndownloader.figshare.com/files/29070555
Source Pub. :
https://doi.org/10.1103/PhysRevMaterials.7.044603
Num. Configurations : 829,576
Num. Atoms : 2,578,920
Num. Elements : 64
JARVIS_QM9_STD_JCTC
Description :
The JARVIS_QM9_STD_JCTC dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains ...
Authors :
Elements :
C, F, H, N, O
Source Data :
https://ndownloader.figshare.com/files/28715319
Source Pub. :
https://doi.org/10.1038/sdata.2014.22
Num. Configurations : 130,829
Num. Atoms : 2,359,192
Num. Elements : 5
JARVIS_QMOF
Description :
The JARVIS_QMOF dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configur...
Authors :
Elements :
Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl, Co...
Source Data :
https://figshare.com/ndownloader/files/30972640
Source Pub. :
https://doi.org/10.1016/j.matt.2021.02.015
Num. Configurations : 20,425
Num. Atoms : 2,321,633
Num. Elements : 79
JARVIS_SNUMAT
Description :
The JARVIS_SNUMAT dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains band g...
Authors :
Elements :
Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://ndownloader.figshare.com/files/38521736
Source Pub. :
https://doi.org/10.1038/s41597-020-00723-8
Num. Configurations : 10,481
Num. Atoms : 216,749
Num. Elements : 73
JARVIS_TinNet_N
Description :
The JARVIS_TinNet dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains config...
Authors :
Elements :
Ag, Au, Cd, Co, Cr, Cu, Fe, H, Hf, Ir, Mn, Mo, N, Nb, Ni...
Source Data :
https://figshare.com/ndownloader/files/40934285
Source Pub. :
https://doi.org/10.1038/s41467-021-25639-8
Num. Configurations : 329
Num. Atoms : 6,251
Num. Elements : 27
JARVIS_TinNet_O
Description :
The JARVIS_TinNet dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains config...
Authors :
Elements :
Ag, Al, Au, Bi, Cd, Co, Cr, Cu, Fe, Ga, Hf, In, Ir, La, ...
Source Data :
https://figshare.com/ndownloader/files/40934285
Source Pub. :
https://doi.org/10.1038/s41467-021-25639-8
Num. Configurations : 747
Num. Atoms : 12,699
Num. Elements : 36
JARVIS_TinNet_OH
Description :
The JARVIS_TinNet dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains config...
Authors :
Elements :
Ag, Al, Au, Bi, Cd, Co, Cr, Cu, Fe, Ga, H, Hf, In, Ir, L...
Source Data :
https://figshare.com/ndownloader/files/40934285
Source Pub. :
https://doi.org/10.1038/s41467-021-25639-8
Num. Configurations : 748
Num. Atoms : 13,464
Num. Elements : 37
JARVIS_mlearn
Description :
The JARVIS_mlearn dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains config...
Authors :
Elements :
Cu, Ge, Li, Mo, Ni, Si
Source Data :
https://figshare.com/ndownloader/files/40424156
Source Pub. :
https://doi.org/10.1021/acs.jpca.9b08723
Num. Configurations : 1,566
Num. Atoms : 115,742
Num. Elements : 6
LiGePS_SSE_PBE
Description :
Approximately 6,500 configurations of Li10GeP2S12, based on crystal structures from the Materials Project database, material ID mp-696129. One of two ...
Authors :
Elements :
Ge, Li, P, S
Source Pub. :
https://doi.org/10.1063/5.0041849
Num. Configurations : 6,550
Num. Atoms : 1,479,000
Num. Elements : 4
LiGePS_SSE_PBEsol
Description :
Approximately 2,800 configurations of Li10GeP2S12, based on crystal structures from the Materials Project database, material ID mp-696129. One of two ...
Authors :
Elements :
Ge, Li, P, S
Source Pub. :
https://doi.org/10.1063/5.0041849
Num. Configurations : 2,835
Num. Atoms : 504,350
Num. Elements : 4
LiSiPS_SSE_PBE
Description :
Approximately 9,100 configurations of Li10SiP2S12, based on crystal structures from the Materials Project database, material ID mp-696129. One of two ...
Authors :
Elements :
Li, P, S, Si
Source Pub. :
https://doi.org/10.1063/5.0041849
Num. Configurations : 9,169
Num. Atoms : 2,101,550
Num. Elements : 4
LiSiPS_SSE_PBEsol
Description :
Approximately 2,300 configurations of Li10SiP2S12, based on crystal structures from the Materials Project database, material ID mp-696129. One of two ...
Authors :
Elements :
Li, P, S, Si
Source Pub. :
https://doi.org/10.1063/5.0041849
Num. Configurations : 2,357
Num. Atoms : 313,150
Num. Elements : 4
LiTiO_Science_2020
Description :
This dataset contains configurations of lithium titanate from the publication Kinetic Pathways of ionic transport in fast-charging lithium titanate. I...
Authors :
Elements :
Be, Li, O, Ti
Source Data :
https://doi.org/10.24435/materialscloud:2020.0006/v1
Source Pub. :
https://doi.org/10.1126/science.aax3520
Num. Configurations : 849
Num. Atoms : 150,105
Num. Elements : 4
MD22_AT_AT
Description :
Dataset containing MD trajectories of AT-AT DNA base pairs from the MD22 benchmark set. {DESC}
Authors :
Elements :
C, H, N, O
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1126/sciadv.adf0873
Num. Configurations : 20,001
Num. Atoms : 1,200,060
Num. Elements : 4
MD22_AT_AT_CG_CG
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 ...
Authors :
Elements :
C, H, N, O
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1126/sciadv.adf0873
Num. Configurations : 10,153
Num. Atoms : 1,198,054
Num. Elements : 4
MD22_Ac_Ala3_NHMe
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...
Authors :
Elements :
C, H, N, O
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1126/sciadv.adf0873
Num. Configurations : 85,109
Num. Atoms : 3,574,578
Num. Elements : 4
MD22_DHA
Description :
Dataset containing MD trajectories of DHA (docosahexaenoic acid) from the MD22 benchmark set. MD22 represents a collection of datasets in a benchmark ...
Authors :
Elements :
C, H, O
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1126/sciadv.adf0873
Num. Configurations : 69,753
Num. Atoms : 3,906,168
Num. Elements : 3
MD22_buckyball_catcher
Description :
Dataset containing MD trajectories of the buckyball-catcher supramolecule from the MD22 benchmark set. MD22 represents a collection of datasets in a b...
Authors :
Elements :
C, H
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1126/sciadv.adf0873
Num. Configurations : 6,102
Num. Atoms : 903,096
Num. Elements : 2
MD22_double_walled_nanotube
Description :
Dataset containing MD trajectories of the double-walled nanotube supramolecule from the MD22 benchmark set. MD22 represents a collection of datasets i...
Authors :
Elements :
C, H
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1126/sciadv.adf0873
Num. Configurations : 5,032
Num. Atoms : 1,861,840
Num. Elements : 2
MD22_stachyose
Description :
Dataset containing MD trajectories of the tetrasaccharide stachyose from the MD22 benchmark set. MD22 represents a collection of datasets in a benchma...
Authors :
Elements :
C, H, O
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1126/sciadv.adf0873
Num. Configurations : 27,272
Num. Atoms : 2,372,664
Num. Elements : 3
MISPR
Description :
Example dataset for MISPR (Materials Informatics for Structure-Property Relationships) materials science simulation software, with DFT-calculated conf...
Authors :
Elements :
C, Cl, F, H, N, O, P, S, Si
Source Data :
https://doi.org/10.1038/s41598-022-20009-w
Source Pub. :
https://github.com/rashatwi/mispr-dataset
Num. Configurations : 503
Num. Atoms : 8,996
Num. Elements : 9
MTPu_2023
Description :
A comprehensive database generated using density functional theory simulations, encompassing a wide range of crystal structures, point defects, extend...
Authors :
Elements :
O, Si
Source Data :
https://gitlab.com/Kazongogit/MTPu
Source Pub. :
https://doi.org/10.48550/arXiv.2311.15170
Num. Configurations : 1,062
Num. Atoms : 71,595
Num. Elements : 2
Matbench_mp_e_form
Description :
Matbench v0.1 test dataset for predicting DFT formation energy from structure. Adapted from Materials Project database. Entries having formation energ...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://matbench.materialsproject.org/
Source Pub. :
https://doi.org/10.1038/s41524-020-00406-3
Num. Configurations : 132,752
Num. Atoms : 3,869,658
Num. Elements : 84
Matbench_mp_gap
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 databas...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://matbench.materialsproject.org/
Source Pub. :
https://doi.org/10.1039/C2EE22341D
Num. Configurations : 106,113
Num. Atoms : 3,184,984
Num. Elements : 84
Matbench_perovskites
Description :
The Matbench_perovskites dataset is a Matbench v0.1 test dataset for predicting formation energy from crystal structure. Adapted from an original data...
Authors :
Elements :
Ag, Al, As, Au, B, Ba, Be, Bi, Ca, Cd, Co, Cr, Cs, Cu, F...
Source Data :
https://matbench.materialsproject.org/
Source Pub. :
https://doi.org/10.1039/C2EE22341D
Num. Configurations : 18,928
Num. Atoms : 94,640
Num. Elements : 56
Materials_Project
Description :
Configurations from the Materials Project database: an online resource with the goal of computing properties of all inorganic materials.
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://materialsproject.org
Source Pub. :
https://doi.org/10.1063/1.4812323
Num. Configurations : 6,342,530
Num. Atoms : 200,016,161
Num. Elements : 89
Mg_edmonds_2022
Description :
16748 configurations of magnesium with gathered energy, stress and forces at the DFT level of theory.
Authors :
Elements :
Mg
Source Data :
https://doi.org/10.17617/3.A3MB7Z
Source Pub. :
https://doi.org/10.1103/PhysRevB.107.104103
Num. Configurations : 16,874
Num. Atoms : 78,617
Num. Elements : 1
MoNbTaVW_PRB2021
Description :
This dataset was originally designed to fit a GAP model for the Mo-Nb-Ta-V-W quinary system that was used to study segregation and defects in the body...
Authors :
Elements :
Mo, Nb, Ta, V, W
Source Pub. :
https://doi.org/10.1103/PhysRevB.104.104101
Num. Configurations : 2,329
Num. Atoms : 127,913
Num. Elements : 5
Mo_PRM2019
Description :
This dataset was designed to enable machine learning of Mo elastic, thermal, and defect properties, as well as surface energetics, melting, and the st...
Authors :
Elements :
Mo
Source Data :
https://gitlab.com/acclab/gap-data/-/tree/master/Mo
Source Pub. :
https://doi.org/10.1103/PhysRevMaterials.4.093802
Num. Configurations : 3,785
Num. Atoms : 45,667
Num. Elements : 1
NDSC_TUT_2022
Description :
500 configurations of Mg2 for MD prediction using a model fitted on Al, W, Mg and Si.
Authors :
Elements :
Mg
Source Data :
https://github.com/ConnorSA/ndsc_tut
Source Pub. :
https://doi.org/10.48550/arXiv.2207.11828
Num. Configurations : 500
Num. Atoms : 1,000
Num. Elements : 1
NENCI-2021
Description :
NENCI-2021 is a database of approximately 8000 benchmark Non-Equilibirum Non-Covalent Interaction (NENCI) energies performed on molecular dimers;inter...
Authors :
Elements :
Br, C, Cl, F, H, Li, N, Na, O, P, S
Source Pub. :
https://doi.org/10.1063/5.0068862
Num. Configurations : 7,763
Num. Atoms : 129,402
Num. Elements : 11
NEP_PRB_2021
Description :
Approximately 7,000 distinct configurations of 2D-silicene, silicon, and PbTe. Silicon data used from http://dx.doi.org/10.1103/PhysRevX.8.041048. Dat...
Authors :
Elements :
Pb, Si, Te
Source Data :
https://doi.org/10.5281/zenodo.5109599
Source Pub. :
https://doi.org/10.1103/PhysRevB.104.104309
Num. Configurations : 7,426
Num. Atoms : 611,808
Num. Elements : 3
NEP_qHPF_test
Description :
The test set of a train and test set pair.The combined datasets comprise approximately 275 configurations of monolayer quasi-hexagonal-phase fullerene...
Authors :
Elements :
C
Source Data :
https://doi.org/10.5281/zenodo.7018572
Source Pub. :
https://doi.org/10.1016/j.eml.2022.101929
Num. Configurations : 39
Num. Atoms : 4,680
Num. Elements : 1
NEP_qHPF_train
Description :
The train set of a train and test set pair.The combined datasets comprise approximately 275 configurations of monolayer quasi-hexagonal-phase fulleren...
Authors :
Elements :
C
Source Data :
https://doi.org/10.5281/zenodo.7018572
Source Pub. :
https://doi.org/10.1016/j.eml.2022.101929
Num. Configurations : 238
Num. Atoms : 28,560
Num. Elements : 1
NMD-18
Description :
3,000 Al-Ga-In sesquioxides with energies and band gaps. Relaxed and Vegard's Law geometries with formation energy and band gaps at DFT-PBE level of t...
Authors :
Elements :
Al, Ga, In, O
Source Data :
https://qmml.org/datasets.html
Source Pub. :
https://doi.org/10.1038/s41524-019-0239-3
Num. Configurations : 3,000
Num. Atoms : 185,070
Num. Elements : 4
NNIP_FeH_PRM_2021
Description :
Approximately 20,000 configurations from a dataset of alpha-iron and hydrogen. Properties include forces and potential energy, calculated using VASP a...
Authors :
Elements :
Fe, H
Source Data :
https://github.com/mengfsou/NNIP-FeH
Source Pub. :
https://doi.org/10.1103/PhysRevMaterials.5.113606
Num. Configurations : 20,920
Num. Atoms : 1,870,008
Num. Elements : 2
NNP-Ga2O3
Description :
9,200 configurations of beta-Ga2O3, including two configuration sets. One contains DFT data for 8400 configurations simulated between temperatures of ...
Authors :
Elements :
Ga, O
Source Data :
https://github.com/RuiyangLi6/NNP_Ga2O3
Source Pub. :
https://doi.org/10.1063/5.0025051
Num. Configurations : 9,200
Num. Atoms : 2,944,000
Num. Elements : 2
NVNMD_GeTe
Description :
Approximately 5,000 configurations of GeTe used in training of a non-von Neumann multiplication-less DNN model.
Authors :
Elements :
Ge, Te
Source Data :
https://github.com/LiuGroupHNU/nvnmd
Source Pub. :
https://doi.org/10.1038/s41524-022-00773-z
Num. Configurations : 5,025
Num. Atoms : 321,600
Num. Elements : 2
N_O_F_columns_non-bonded_vdW_potential_JCP2023
Description :
This dataset contains structures of materials from the N (15th), O (16th) and F (16th) columns of the periodic table used for generating a 2-body non-...
Authors :
Elements :
As, At, Bi, O, P, Po, S, Sb, Se, Te
Source Data :
https://doi.org/10.1063/5.0174188
Source Pub. :
https://doi.org/10.1063/5.0174188
Num. Configurations : 262
Num. Atoms : 1,494
Num. Elements : 10
Nb_PRM2019
Description :
This dataset was designed to enable machine-learning of Nb elastic, thermal, and defect properties, as well as surface energetics, melting, and the st...
Authors :
Elements :
Nb
Source Data :
https://gitlab.com/acclab/gap-data/-/tree/master/
Source Pub. :
https://doi.org/10.1103/PhysRevMaterials.4.093802
Num. Configurations : 3,787
Num. Atoms : 45,641
Num. Elements : 1
NequIP_NC_2022
Description :
Approximately 57,000 configurations from the evaluation datasets for NequIP graph neural network model for interatomic potentials. Trajectories have b...
Authors :
Elements :
C, Cu, H, Li, O, P, S
Source Data :
https://doi.org/10.24435/materialscloud:s0-5n
Source Pub. :
https://doi.org/10.1038/s41467-022-29939-5
Num. Configurations : 56,856
Num. Atoms : 7,631,075
Num. Elements : 7
NiCoCr_NC2020
Description :
The face-centered cubic medium-entropy alloy NiCoCr has received considerable attention for its good mechanical properties, uncertain stacking fault e...
Authors :
Elements :
Co, Cr, Ni
Source Data :
https://doi.org/10.24435/materialscloud:s4-g3
Source Pub. :
https://doi.org/10.1038/s41467-020-16083-1
Num. Configurations : 428
Num. Atoms : 40,624
Num. Elements : 3
OC20_IS2RES_train
Description :
This dataset contains all frames from the trajectories for the training configurations in the OC20 initial structure to relaxed energy (IS2RE) and ini...
Authors :
Elements :
Ag, Al, As, Au, B, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe...
Source Data :
https://fair-chem.github.io/core/datasets/oc20.html
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 92,899,663
Num. Atoms : 7,522,707,627
Num. Elements : 56
OC20_IS2RES_val_id
Description :
OC20_IS2RES_val_id is the in-domain validation set for the OC20 Initial Structure to Relaxed Structure (IS2RS) and Initial Structure to Relaxed Energy...
Authors :
Elements :
Ag, Al, As, Au, B, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe...
Source Data :
https://fair-chem.github.io/core/datasets/oc20.html
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 5,026,511
Num. Atoms : 406,702,097
Num. Elements : 56
OC20_IS2RES_val_ood_ads
Description :
OC20_IS2RES_ood_ads is the out-of-domain validation set for the OC20 Initial Structure to Relaxed Structure (IS2RS) and Initial Structure to Relaxed E...
Authors :
Elements :
Ag, Al, As, Au, B, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe...
Source Data :
https://fair-chem.github.io/core/datasets/oc20.html
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 4,883,196
Num. Atoms : 390,308,139
Num. Elements : 56
OC20_IS2RES_val_ood_both
Description :
OC20_IS2RES_ood_ads is the out-of-domain validation set for the OC20 Initial Structure to Relaxed Structure (IS2RS) and Initial Structure to Relaxed E...
Authors :
Elements :
Ag, Al, As, Au, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe, G...
Source Data :
https://fair-chem.github.io/core/datasets/oc20.html
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 3,665,193
Num. Atoms : 308,297,930
Num. Elements : 55
OC20_IS2RES_val_ood_cat
Description :
OC20_IS2RES_val_ood_cat is the out-of-domain validation set for the OC20 Initial Structure to Relaxed Structure (IS2RS) and Initial Structure to Relax...
Authors :
Elements :
Ag, Al, As, Au, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe, G...
Source Data :
https://fair-chem.github.io/core/datasets/oc20.html
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 5,151,015
Num. Atoms : 411,767,380
Num. Elements : 55
OC20_S2EF_train_200K
Description :
OC20_S2EF_train_200K is the 200K training split of the OC20 Structure to Energy and Forces (S2EF) task.
Authors :
Elements :
Ag, Al, As, Au, B, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe...
Source Data :
https://fair-chem.github.io/core/datasets/oc20.html
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 200,000
Num. Atoms : 14,631,937
Num. Elements : 56
OC20_S2EF_train_20M
Description :
OC20_S2EF_train_20M is the 20 million structure training subset of the OC20 Structure to Energy and Forces dataset. Features include potential energy,...
Authors :
Elements :
Ag, Al, As, Au, B, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe...
Source Data :
https://fair-chem.github.io/core/datasets/oc20.html
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 20,000,000
Num. Atoms : 1,465,265,878
Num. Elements : 56
OC20_S2EF_train_2M
Description :
OC20_S2EF_train_2M is the 2 million structure training subset of the OC20 Structure to Energy and Forces dataset. Features include potential energy, f...
Authors :
Elements :
Ag, Al, As, Au, B, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe...
Source Data :
https://fair-chem.github.io/core/datasets/oc20.html
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 2,000,000
Num. Atoms : 146,496,199
Num. Elements : 56
OC20_S2EF_train_all
Description :
OC20_S2EF_train_all is the ~63 million structure full training set of the OC20 Structure to Energy and Forces (S2EF) dataset. Features include energy,...
Authors :
Elements :
Ag, Al, As, Au, B, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe...
Source Data :
https://fair-chem.github.io/core/datasets/oc20.html
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 133,934,018
Num. Atoms : 9,810,895,377
Num. Elements : 56
OC20_S2EF_val_id
Description :
OC20_S2EF_val_id is the ~1-million structure in-domain validation set of the OC20 Structure to Energy and Forces (S2EF) dataset. Features include ener...
Authors :
Elements :
Ag, Al, As, Au, B, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe...
Source Data :
https://fair-chem.github.io/core/datasets/oc20.html
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 999,866
Num. Atoms : 73,147,343
Num. Elements : 56
OC20_S2EF_val_ood_ads
Description :
OC20_S2EF_val_ood_ads is the out-of-domain validation set of the OC20 Structure to Energy and Forces (S2EF) dataset featuring unseen adsorbate. Featur...
Authors :
Elements :
Ag, Al, As, Au, B, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe...
Source Data :
https://fair-chem.github.io/core/datasets/oc20.html
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 999,838
Num. Atoms : 72,858,155
Num. Elements : 56
OC20_S2EF_val_ood_both
Description :
OC20_S2EF_val_ood_both is the out-of-domain validation set of the OC20 Structure to Energy and Forces (S2EF) dataset featuring both unseen catalyst co...
Authors :
Elements :
Ag, Al, As, Au, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe, G...
Source Data :
https://fair-chem.github.io/core/datasets/oc20.html
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 999,944
Num. Atoms : 84,604,635
Num. Elements : 55
OC20_S2EF_val_ood_cat
Description :
OC20_S2EF_val_ood_cat is the out-of-domain validation set of the OC20 Structure to Energy and Forces (S2EF) dataset featuring unseen catalyst composit...
Authors :
Elements :
Ag, Al, As, Au, Bi, C, Ca, Cd, Cl, Co, Cr, Cs, Cu, Fe, G...
Source Data :
https://fair-chem.github.io/core/datasets/oc20.html
Source Pub. :
https://doi.org/10.1021/acscatal.0c04525
Num. Configurations : 999,809
Num. Atoms : 74,059,718
Num. Elements : 55
OC22-IS2RE-Train
Description :
Training configurations for the initial structure to relaxed total energy (IS2RE) task of OC22. Open Catalyst 2022 (OC22) is a database of training tr...
Authors :
Elements :
Ag, Al, As, Au, Ba, Be, Bi, C, Ca, Cd, Ce, Co, Cr, Cs, C...
Source Data :
https://github.com/Open-Catalyst-Project/ocp/blob/main/DATASET.md#open-catalyst-2022-oc22
Source Pub. :
https://doi.org/10.1021/acscatal.2c05426
Num. Configurations : 7,878,688
Num. Atoms : 634,845,874
Num. Elements : 57
OC22-IS2RE-Validation-in-domain
Description :
In-domain validation configurations for the initial structure to relaxed total energy (IS2RE) task of OC22. Open Catalyst 2022 (OC22) is a database of...
Authors :
Elements :
Ag, Al, As, Au, Ba, Be, Bi, C, Ca, Cd, Ce, Co, Cr, Cs, C...
Source Data :
https://github.com/Open-Catalyst-Project/ocp/blob/main/DATASET.md#open-catalyst-2022-oc22
Source Pub. :
https://doi.org/10.1021/acscatal.2c05426
Num. Configurations : 442,843
Num. Atoms : 35,275,211
Num. Elements : 57
OC22-IS2RE-Validation-out-of-domain
Description :
Out-of-domain validation configurations for the initial structure to relaxed total energy (IS2RE) task of OC22. Open Catalyst 2022 (OC22) is a databas...
Authors :
Elements :
Au, Ba, Be, Bi, C, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, G...
Source Data :
https://github.com/Open-Catalyst-Project/ocp/blob/main/DATASET.md#open-catalyst-2022-oc22
Source Pub. :
https://doi.org/10.1021/acscatal.2c05426
Num. Configurations : 521,827
Num. Atoms : 42,219,955
Num. Elements : 52
OC22-S2EF-Train
Description :
Training configurations for the structure to total energy and forces task (S2EF) of OC22. Open Catalyst 2022 (OC22) is a database of training trajecto...
Authors :
Elements :
Ag, Al, As, Au, Ba, Be, Bi, C, Ca, Cd, Ce, Co, Cr, Cs, C...
Source Data :
https://github.com/Open-Catalyst-Project/ocp/blob/main/DATASET.md#open-catalyst-2022-oc22
Source Pub. :
https://doi.org/10.1021/acscatal.2c05426
Num. Configurations : 8,389,365
Num. Atoms : 669,615,870
Num. Elements : 57
OC22-S2EF-Validation-in-domain
Description :
In-domain validation configurations for the structure to total energy and forces (S2EF) task of OC22. Open Catalyst 2022 (OC22) is a database of train...
Authors :
Elements :
Ag, Al, As, Au, Ba, Be, Bi, C, Ca, Cd, Ce, Co, Cr, Cs, C...
Source Data :
https://github.com/Open-Catalyst-Project/ocp/blob/main/DATASET.md#open-catalyst-2022-oc22
Source Pub. :
https://doi.org/10.1021/acscatal.2c05426
Num. Configurations : 407,195
Num. Atoms : 31,919,751
Num. Elements : 57
OC22-S2EF-Validation-out-of-domain
Description :
Out-of-domain validation configurations for the structure to total energy and forces (S2EF) task of OC22. Open Catalyst 2022 (OC22) is a database of t...
Authors :
Elements :
Au, Ba, Be, Bi, C, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, G...
Source Data :
https://github.com/Open-Catalyst-Project/ocp/blob/main/DATASET.md#open-catalyst-2022-oc22
Source Pub. :
https://doi.org/10.1021/acscatal.2c05426
Num. Configurations : 459,594
Num. Atoms : 36,999,141
Num. Elements : 52
OMat24_train_aimd_from_PBE_1000_npt
Description :
The aimd-from-PBE-1000-npt training split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) cal...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 21,269,486
Num. Atoms : 179,930,890
Num. Elements : 89
OMat24_train_aimd_from_PBE_1000_nvt
Description :
The aimd-from-PBE-1000-nvt training split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) cal...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 20,256,650
Num. Atoms : 169,879,539
Num. Elements : 86
OMat24_train_aimd_from_PBE_3000_npt
Description :
The aimd-from-PBE-3000-npt training split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) cal...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 6,076,290
Num. Atoms : 411,540,573
Num. Elements : 89
OMat24_train_aimd_from_PBE_3000_nvt
Description :
The aimd-from-PBE-3000-nvt training split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) cal...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 7,839,846
Num. Atoms : 530,963,613
Num. Elements : 86
OMat24_train_rattled_1000
Description :
The rattled-1000 training split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) calculations....
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 11,388,475
Num. Atoms : 161,511,768
Num. Elements : 89
OMat24_train_rattled_1000_subsampled
Description :
The rattled-1000-subsampled training split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) ca...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 3,879,731
Num. Atoms : 55,648,760
Num. Elements : 89
OMat24_train_rattled_300
Description :
The rattled-300 training split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) calculations. ...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 6,319,086
Num. Atoms : 89,791,956
Num. Elements : 88
OMat24_train_rattled_300_subsampled
Description :
The rattled-300-subsampled training split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) cal...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 3,463,993
Num. Atoms : 49,674,369
Num. Elements : 88
OMat24_train_rattled_500
Description :
The rattled-500 training split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) calculations. ...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 6,922,152
Num. Atoms : 98,860,290
Num. Elements : 88
OMat24_train_rattled_500_subsampled
Description :
The rattled-500-subsampled training split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) cal...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 3,975,399
Num. Atoms : 56,846,329
Num. Elements : 89
OMat24_train_rattled_relax
Description :
The rattled-relax training split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) calculations...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 9,433,298
Num. Atoms : 78,952,123
Num. Elements : 87
OMat24_validation_aimd-from-PBE-1000-npt
Description :
The val_aimd-from-PBE-1000-npt validation split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DF...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 202,758
Num. Atoms : 1,710,254
Num. Elements : 85
OMat24_validation_aimd-from-PBE-1000-nvt
Description :
The val_aimd-from-PBE-1000-nvt validation split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DF...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 195,575
Num. Atoms : 1,643,554
Num. Elements : 85
OMat24_validation_aimd-from-PBE-3000-npt
Description :
The val_aimd-from-PBE-3000-npt validation split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DF...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 59,516
Num. Atoms : 4,036,396
Num. Elements : 85
OMat24_validation_aimd-from-PBE-3000-nvt
Description :
The val_aimd-from-PBE-3000-nvt validation split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DF...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 76,478
Num. Atoms : 5,186,115
Num. Elements : 84
OMat24_validation_rattled_1000
Description :
The rattled-1000 validation split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) calculation...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 117,004
Num. Atoms : 1,657,765
Num. Elements : 86
OMat24_validation_rattled_1000_subsampled
Description :
The rattled-1000-subsampled validation split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) ...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 38,271
Num. Atoms : 549,832
Num. Elements : 87
OMat24_validation_rattled_300
Description :
The rattled-300 validation split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) calculations...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 62,451
Num. Atoms : 883,431
Num. Elements : 84
OMat24_validation_rattled_300_subsampled
Description :
The rattled-300-subsampled validation split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) c...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 34,244
Num. Atoms : 490,880
Num. Elements : 85
OMat24_validation_rattled_500
Description :
The rattled-500 validation split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) calculations...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 68,830
Num. Atoms : 985,338
Num. Elements : 85
OMat24_validation_rattled_500_subsampled
Description :
The rattled-500-subsampled validation split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) c...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 39,464
Num. Atoms : 564,068
Num. Elements : 85
OMat24_validation_rattled_relax
Description :
The rattled-relax validation split of OMat24 (Open Materials 2024). OMat24 is a large-scale open dataset of density functional theory (DFT) calculatio...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 91,043
Num. Atoms : 764,266
Num. Elements : 84
OMol25_neutral_validation
Description :
The neutral validation set from OMol25. From the dataset creator: OMol25 represents the largest high quality molecular DFT dataset spanning biomolecul...
Authors :
Elements :
B, Br, C, Ca, Cl, F, H, I, K, Li, Mg, N, Na, O, P, S, Si
Source Data :
https://huggingface.co/facebook/OMol25
Source Pub. :
https://doi.org/10.48550/arXiv.2505.08762
Num. Configurations : 27,697
Num. Atoms : 1,238,644
Num. Elements : 17
OMol25_train
Description :
The full-size training set from OMol25. From the dataset creator: OMol25 represents the largest high quality molecular DFT dataset spanning biomolecul...
Authors :
Elements :
Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://huggingface.co/facebook/OMol25
Source Pub. :
https://doi.org/10.48550/arXiv.2505.08762
Num. Configurations : 101,666,280
Num. Atoms : 5,237,539,207
Num. Elements : 83
OMol25_train_4M
Description :
The Train 4M set from OMol25 (~4 million structure training subset). From the dataset creator: OMol25 represents the largest high quality molecular DF...
Authors :
Elements :
Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://huggingface.co/facebook/OMol25
Source Pub. :
https://doi.org/10.48550/arXiv.2505.08762
Num. Configurations : 3,986,754
Num. Atoms : 218,680,957
Num. Elements : 83
OMol25_train_neutral
Description :
The Train neutral set from OMol25. From the dataset creator: OMol25 represents the largest high quality molecular DFT dataset spanning biomolecules, m...
Authors :
Elements :
B, Br, C, Ca, Cl, F, H, I, K, Li, Mg, N, Na, O, P, S, Si
Source Data :
https://huggingface.co/facebook/OMol25
Source Pub. :
https://doi.org/10.48550/arXiv.2505.08762
Num. Configurations : 34,335,828
Num. Atoms : 929,562,799
Num. Elements : 17
OMol25_validation
Description :
The validation set from OMol25. From the dataset creator: OMol25 represents the largest high quality molecular DFT dataset spanning biomolecules, meta...
Authors :
Elements :
Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://huggingface.co/facebook/OMol25
Source Pub. :
https://doi.org/10.48550/arXiv.2505.08762
Num. Configurations : 2,762,021
Num. Atoms : 283,298,012
Num. Elements : 83
OrbNet_Denali
Description :
All DFT single-point calculations for the OrbNet Denali training set were carried out in Entos Qcore version 0.8.17 at the ωB97X-D3/def2-TZVP level of...
Authors :
Elements :
B, Br, C, Ca, Cl, F, H, I, K, Li, Mg, N, Na, O, P, S, Si
Source Data :
https://doi.org/10.6084/m9.figshare.14883867.v2
Source Pub. :
https://doi.org/10.1063/5.0061990
Num. Configurations : 2,338,215
Num. Atoms : 104,958,650
Num. Elements : 17
PWMLFF_feature_comparison_NPJ2023
Description :
Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on ...
Authors :
Elements :
C, H, Mg, Ni, O, Si
Source Pub. :
https://www.doi.org/10.1088/1367-2630/acf2bb
Num. Configurations : 17,255
Num. Atoms : 918,240
Num. Elements : 6
Paramagnetic_lanthanide_compounds
Description :
This dataset is composed of fully-deuterated Gd(III) analogue d-[GdL] in a variety of solvent materials, including MeOH, D2O and d6-DMSO.
Authors :
Elements :
C, Gd, H, N, O, S
Source Data :
https://doi.org/10.1021/jacs.3c01342
Source Pub. :
https://doi.org/10.48420/22015322.v1
Num. Configurations : 41,748
Num. Atoms : 28,419,876
Num. Elements : 6
PropMolFlow_QM9_CNOFH_2025
Description :
This DFT dataset is curated in response to the growing interest in property-guided molecule genaration using generative AI models. Typically, the prop...
Authors :
Elements :
C, F, H, N, O
Source Pub. :
https://arxiv.org/abs/2505.21469
Num. Configurations : 10,773
Num. Atoms : 205,304
Num. Elements : 5
PtNi_alloy_NPJ2022
Description :
DFT dataset consisting of 6828 resampled Pt-Ni alloys used for training an NNP. The energy and forces of each structure in the resampled database are ...
Authors :
Elements :
Ni, Pt
Source Data :
https://zenodo.org/record/5645281#.Y2CPkeTMJEa
Source Pub. :
https://doi.org/10.1038/s41524-022-00807-6
Num. Configurations : 6,828
Num. Atoms : 1,074,161
Num. Elements : 2
QM-22
Description :
Includes CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader co...
Authors :
Elements :
C, H, O
Source Data :
https://github.com/jmbowma/QM-22
Source Pub. :
https://doi.org/10.1063/5.0089200
Num. Configurations : 6,762
Num. Atoms : 101,430
Num. Elements : 3
QM7b_AlphaML
Description :
Energy, computed with LR-CCSD, hybrid DFT (B3LYP & SCAN0) for 7211 molecules in QM7b and 52 molecules in AlphaML showcase database.
Authors :
Elements :
C, Cl, H, N, O, S
Source Data :
https://doi.org/10.24435/materialscloud:2019.0002/v3
Source Pub. :
https://doi.org/10.1038/s41597-019-0157-8
Num. Configurations : 29,033
Num. Atoms : 408,865
Num. Elements : 6
QM9x
Description :
Dataset containing DFT calculations of energy and forces for all configurations in the QM9 dataset, recalculated with the ωB97X functional and 6-31G(d...
Authors :
Elements :
C, F, H, N, O
Source Data :
https://doi.org/10.6084/m9.figshare.20449701.v2
Source Pub. :
https://doi.org/10.1038/s41597-022-01870-w
Num. Configurations : 133,885
Num. Atoms : 2,407,753
Num. Elements : 5
QM_hamiltonian_nature_2019
Description :
~100,000 configurations of water, ethanol, malondialdehyde and uracil gathered at the PBE/def2-SVP level of theory using ORCA.
Authors :
Elements :
C, H, N, O
Source Data :
http://quantum-machine.org/datasets/
Source Pub. :
https://doi.org/10.1038/s41467-019-12875-2
Num. Configurations : 91,977
Num. Atoms : 887,799
Num. Elements : 4
REANN_CO2_Ni100
Description :
Approximately 9,850 configurations of CO2 with a movable Ni(100) surface.
Authors :
Elements :
C, Ni, O
Source Data :
https://github.com/zhangylch/REANN
Source Pub. :
https://doi.org/10.1021/acs.jpclett.9b00085
Num. Configurations : 9,850
Num. Atoms : 384,150
Num. Elements : 3
SAIT_semiconductors_ACS_2023_HfO_out-of-domain
Description :
Out-of-domain configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors ...
Authors :
Elements :
Hf, O
Source Data :
https://github.com/SAITPublic/MLFF-Framework
Source Pub. :
https://openreview.net/forum?id=hr9Bd1A9Un
Num. Configurations : 6,996
Num. Atoms : 671,616
Num. Elements : 2
SAIT_semiconductors_ACS_2023_HfO_raw
Description :
Structures from the SAIT_semiconductors_ACS_2023_HfO dataset, separated into crystal, out-of-domain, and random (generated by randomly distributing 32...
Authors :
Elements :
Hf, O
Source Data :
https://github.com/SAITPublic/MLFF-Framework
Source Pub. :
https://openreview.net/forum?id=hr9Bd1A9Un
Num. Configurations : 192,000
Num. Atoms : 18,431,808
Num. Elements : 2
SAIT_semiconductors_ACS_2023_HfO_test
Description :
Test configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets....
Authors :
Elements :
Hf, O
Source Data :
https://github.com/SAITPublic/MLFF-Framework
Source Pub. :
https://openreview.net/forum?id=hr9Bd1A9Un
Num. Configurations : 3,510
Num. Atoms : 336,960
Num. Elements : 2
SAIT_semiconductors_ACS_2023_HfO_train
Description :
Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datas...
Authors :
Elements :
Hf, O
Source Data :
https://github.com/SAITPublic/MLFF-Framework
Source Pub. :
https://openreview.net/forum?id=hr9Bd1A9Un
Num. Configurations : 27,960
Num. Atoms : 2,684,160
Num. Elements : 2
SAIT_semiconductors_ACS_2023_HfO_validation
Description :
Validation configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors dat...
Authors :
Elements :
Hf, O
Source Data :
https://github.com/SAITPublic/MLFF-Framework
Source Pub. :
https://openreview.net/forum?id=hr9Bd1A9Un
Num. Configurations : 3,510
Num. Atoms : 336,960
Num. Elements : 2
SAIT_semiconductors_ACS_2023_SiN_out-of-domain
Description :
Out-of-domain configurations from the SAIT_semiconductors_ACS_2023_SiN dataset. This dataset contains SiN, Si and N configurations from the SAIT semic...
Authors :
Elements :
N, Si
Source Data :
https://github.com/SAITPublic/MLFF-Framework
Source Pub. :
https://openreview.net/forum?id=hr9Bd1A9Un
Num. Configurations : 1,235
Num. Atoms : 129,675
Num. Elements : 2
SAIT_semiconductors_ACS_2023_SiN_raw
Description :
Structures from the SAIT_semiconductors_ACS_2023_SiN dataset, separated into N-only, Si-only, SiN, and out-of-domain melt, quench and relax configurat...
Authors :
Elements :
N, Si
Source Data :
https://github.com/SAITPublic/MLFF-Framework
Source Pub. :
https://openreview.net/forum?id=hr9Bd1A9Un
Num. Configurations : 88,163
Num. Atoms : 5,204,822
Num. Elements : 2
SAIT_semiconductors_ACS_2023_SiN_test
Description :
Test configurations from the SAIT_semiconductors_ACS_2023_SiN dataset. This dataset contains SiN, Si and N configurations from the SAIT semiconductors...
Authors :
Elements :
N, Si
Source Data :
https://github.com/SAITPublic/MLFF-Framework
Source Pub. :
https://openreview.net/forum?id=hr9Bd1A9Un
Num. Configurations : 2,866
Num. Atoms : 165,559
Num. Elements : 2
SAIT_semiconductors_ACS_2023_SiN_train
Description :
Training configurations from the SAIT_semiconductors_ACS_2023_SiN dataset. This dataset contains SiN, Si and N configurations from the SAIT semiconduc...
Authors :
Elements :
N, Si
Source Data :
https://github.com/SAITPublic/MLFF-Framework
Source Pub. :
https://openreview.net/forum?id=hr9Bd1A9Un
Num. Configurations : 22,510
Num. Atoms : 1,284,467
Num. Elements : 2
SAIT_semiconductors_ACS_2023_SiN_validation
Description :
Validation configurations from the SAIT_semiconductors_ACS_2023_SiN dataset. This dataset contains SiN, Si and N configurations from the SAIT semicond...
Authors :
Elements :
N, Si
Source Data :
https://github.com/SAITPublic/MLFF-Framework
Source Pub. :
https://openreview.net/forum?id=hr9Bd1A9Un
Num. Configurations : 2,822
Num. Atoms : 159,951
Num. Elements : 2
SIMPLE_NN_SiO2
Description :
10,000 configurations of SiO2 used as an example for the SIMPLE-NN machine learning model. Dataset includes three types of crystals: quartz, cristobal...
Authors :
Elements :
O, Si
Source Data :
https://doi.org/10.17632/pjv2yr7pvr.1
Source Pub. :
https://doi.org/10.1016/j.cpc.2019.04.014
Num. Configurations : 10,000
Num. Atoms : 600,000
Num. Elements : 2
SN2_JCTC_2019
Description :
The SN2 dataset was generated as a partner benchmark dataset, along with the 'solvated protein fragments' dataset, for measuring the performance of ma...
Authors :
Elements :
Br, C, Cl, F, H, I
Source Data :
https://doi.org/10.5281/zenodo.2605341
Source Pub. :
https://doi.org/10.1021/acs.jctc.9b00181
Num. Configurations : 394,684
Num. Atoms : 2,194,246
Num. Elements : 6
SPICE_2023
Description :
SPICE (Small-Molecule/Protein Interaction Chemical Energies) is a collection of quantum mechanical data for training potential functions. The emphasis...
Authors :
Elements :
Br, C, Ca, Cl, F, H, I, K, Li, N, Na, O, P, S
Source Data :
https://doi.org/10.5281/zenodo.8222043
Source Pub. :
https://doi.org/10.1038/s41597-022-01882-6
Num. Configurations : 116,504
Num. Atoms : 3,382,829
Num. Elements : 14
Si-H-GAP_reference
Description :
A reference set of configurations of hydrogenated liquid and amorphous silicon from the datasets for Si-H-GAP. These configurations were used to evalu...
Authors :
Elements :
H, Si
Source Data :
https://github.com/dgunruh/Si-H-GAP
Source Pub. :
https://doi.org/10.1103/PhysRevMaterials.6.065603
Num. Configurations : 114
Num. Atoms : 24,895
Num. Elements : 2
Si-H-GAP_training
Description :
A set of training configurations of hydrogenated liquid and amorphous silicon from the datasets for Si-H-GAP. Includes virial sigmas used for configur...
Authors :
Elements :
H, Si
Source Data :
https://github.com/dgunruh/Si-H-GAP
Source Pub. :
https://doi.org/10.1103/PhysRevMaterials.6.065603
Num. Configurations : 392
Num. Atoms : 65,909
Num. Elements : 2
Si-H-GAP_validation
Description :
A set of validation configurations of hydrogenated liquid and amorphous silicon from the datasets for Si-H-GAP. These configurations served to augment...
Authors :
Elements :
H, Si
Source Data :
https://github.com/dgunruh/Si-H-GAP
Source Pub. :
https://doi.org/10.1103/PhysRevMaterials.6.065603
Num. Configurations : 150
Num. Atoms : 23,000
Num. Elements : 2
Si_Al_Ti_Seko_PRB_2019_test
Description :
Training sets from Si_Al_Ti_Seko_PRB_2019. This dataset is compiled of 10,000 selected structures from the ICSD, divided into training and test sets. ...
Authors :
Elements :
Al, Si, Ti
Source Pub. :
https://doi.org/10.1103/PhysRevB.99.214108
Num. Configurations : 3,989
Num. Atoms : 197,628
Num. Elements : 3
Si_Al_Ti_Seko_PRB_2019_train
Description :
Test sets from Si_Al_Ti_Seko_PRB_2019. This dataset is compiled of 10,000 selected structures from the ICSD, divided into training and test sets. The ...
Authors :
Elements :
Al, Si, Ti
Source Pub. :
https://doi.org/10.1103/PhysRevB.99.214108
Num. Configurations : 36,155
Num. Atoms : 1,774,664
Num. Elements : 3
Si_JCP_2017
Description :
A dataset of 64-atom silicon configurations in four phases: cubic-diamond, (beta)-tin, R8, and liquid. MD simulations are run at 300, 600 and 900 K fo...
Authors :
Elements :
Si
Source Data :
https://doi.org/10.1063/1.4990503
Source Pub. :
https://doi.org/10.1063/1.4990503
Num. Configurations : 1,117
Num. Atoms : 71,424
Num. Elements : 1
Si_PRX_GAP
Description :
The original DFT training data for the general-purpose silicon interatomic potential described in the associated publication. The kinds of configurati...
Authors :
Elements :
Si
Source Data :
https://doi.org/10.17863/CAM.65004
Source Pub. :
https://doi.org/10.1103/PhysRevX.8.041048
Num. Configurations : 2,472
Num. Atoms : 171,164
Num. Elements : 1
Silica_NPJCM_2022
Description :
This dataset was created for the purpose of training an MLIP for silica (SiO2). For initial DFT computations, GPAW (in combination with ASE) was used ...
Authors :
Elements :
O, Si
Source Data :
https://doi.org/10.5281/zenodo.6353683
Source Pub. :
https://doi.org/10.1038/s41524-022-00768-w
Num. Configurations : 3,074
Num. Atoms : 268,118
Num. Elements : 2
Sn-SCAN_PRM_2023
Description :
Approximately 6,500 configurations of Sn, including Sn8, Sn16 and Sn32, used in developing a deep potential that predicts the phase diagram of Sn.
Authors :
Elements :
Sn
Source Pub. :
https://doi.org/10.1103/PhysRevMaterials.7.053603
Num. Configurations : 6,721
Num. Atoms : 113,584
Num. Elements : 1
TSFF_PLOS_2022
Description :
One configuration of an enzyme: training data for a quantum-guided molecular mechanics model.
Authors :
Elements :
C, H, N, O, S
Source Data :
https://doi.org/10.1371/journal.pone.0264960.s001
Source Pub. :
https://doi.org/10.1371/journal.pone.0264960
Num. Configurations : 1
Num. Atoms : 117
Num. Elements : 5
Ta_Linear_JCP2014
Description :
This data set was originally used to generate a linear SNAP potential for solid and liquid tantalum as published in Thompson, A.P. et. al, J. Comp. Ph...
Authors :
Elements :
Ta
Source Pub. :
https://doi.org/10.1016/j.jcp.2014.12.018
Num. Configurations : 363
Num. Atoms : 4,224
Num. Elements : 1
Ta_PINN_2021
Description :
A dataset consisting of the energies of supercells containing from 1 to 250 atoms. The supercells represent energy-volume relations for 8 crystal stru...
Authors :
Elements :
Ta
Source Data :
https://doi.org/10.1016/j.commatsci.2021.111180
Source Pub. :
https://doi.org/10.1016/j.commatsci.2021.111180
Num. Configurations : 3,196
Num. Atoms : 136,037
Num. Elements : 1
Ta_PRM2019
Description :
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the st...
Authors :
Elements :
Ta
Source Data :
https://gitlab.com/acclab/gap-data/-/tree/master
Source Pub. :
https://doi.org/10.1103/PhysRevMaterials.4.093802
Num. Configurations : 3,775
Num. Atoms : 45,439
Num. Elements : 1
TdS-PdV_Atari5200
Description :
Approximately 45,000 configurations of metal oxides of Mg, Ag, Pt, Cu and Zn, with initial training structures taken from the Materials Project databa...
Authors :
Elements :
Ag, Cu, Mg, O, Pt, Zn
Source Data :
https://doi.org/10.5281/zenodo.7278341
Source Pub. :
https://doi.org/10.1021/acs.jpclett.2c03445
Num. Configurations : 44,404
Num. Atoms : 1,987,604
Num. Elements : 6
TiMoS_alloys_CMS2021
Description :
Training set (DFT output) for CE models and MC simulation output for the manuscript 'Phase behaviour of (Ti:Mo)S2binary alloys arising from electron-l...
Authors :
Elements :
Mo, S, Ti
Source Data :
https://eprints.soton.ac.uk/443461/
Source Pub. :
https://doi.org/10.1016/j.commatsci.2020.110044
Num. Configurations : 259
Num. Atoms : 3,996
Num. Elements : 3
TiO2_CMS2016
Description :
TiO2 dataset that was designed to build atom neural network potentials (ANN) by Artrith et al. using the AENET package. This dataset includes various ...
Authors :
Elements :
O, Ti
Source Data :
https://github.com/DescriptorZoo/sensitivity-dimensionality-results/tree/master/datasets/TiO2
Source Pub. :
https://doi.org/10.1016/j.commatsci.2015.11.047
Num. Configurations : 7,812
Num. Atoms : 165,114
Num. Elements : 2
TiZrHfTa_APS2021
Description :
A dataset used to train machine-learning interatomic potentials (moment tensor potentials) for multicomponent alloys to ab initio data in order to inv...
Authors :
Elements :
Hf, Ta, Ti, Zr
Source Pub. :
https://doi.org/10.1103/PhysRevMaterials.5.073801
Num. Configurations : 3,623
Num. Atoms : 223,984
Num. Elements : 4
Ti_NPJCM_2021
Description :
Approximately 7,400 configurations of titanium used for training a deep potential using the DeePMD-kit molecular dynamics package and DP-GEN training ...
Authors :
Elements :
Ti
Source Pub. :
https://doi.org/10.1038/s41524-021-00661-y
Num. Configurations : 7,378
Num. Atoms : 143,856
Num. Elements : 1
Transition1x-test
Description :
The test split of the Transition1x dataset. Transition1x is a benchmark dataset containing 9.6 million Density Functional Theory (DFT) calculations of...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.6084/m9.figshare.19614657.v4
Source Pub. :
https://doi.org/10.1038/s41597-022-01870-w
Num. Configurations : 190,277
Num. Atoms : 2,106,770
Num. Elements : 4
Transition1x-validation
Description :
The validation split of the Transition1x dataset. Transition1x is a benchmark dataset containing 9.6 million Density Functional Theory (DFT) calculati...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.6084/m9.figshare.19614657.v4
Source Pub. :
https://doi.org/10.1038/s41597-022-01870-w
Num. Configurations : 264,996
Num. Atoms : 3,743,476
Num. Elements : 4
Transition1x_train
Description :
The training split of the Transition1x dataset. Transition1x is a benchmark dataset containing 9.6 million Density Functional Theory (DFT) calculation...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.6084/m9.figshare.19614657.v4
Source Pub. :
https://doi.org/10.1038/s41597-022-01870-w
Num. Configurations : 62,990
Num. Atoms : 536,010
Num. Elements : 4
UNEP_v1_2023_test
Description :
The test set for UNEP-v1 (version 1 of Unified NeuroEvolution Potential), a model implemented in GPUMD.
Authors :
Elements :
Ag, Al, Au, Cr, Cu, Mg, Mo, Ni, Pb, Pd, Pt, Ta, Ti, V, W...
Source Data :
https://zenodo.org/doi/10.5281/zenodo.10081676
Source Pub. :
https://doi.org/10.48550/arXiv.2311.04732
Num. Configurations : 4,411
Num. Atoms : 318,910
Num. Elements : 16
UNEP_v1_2023_train
Description :
The training set for UNEP-v1 (version 1 of Unified NeuroEvolution Potential), a model implemented in GPUMD.
Authors :
Elements :
Ag, Al, Au, Cr, Cu, Mg, Mo, Ni, Pb, Pd, Pt, Ta, Ti, V, W...
Source Data :
https://zenodo.org/doi/10.5281/zenodo.10081676
Source Pub. :
https://doi.org/10.48550/arXiv.2311.04732
Num. Configurations : 104,799
Num. Atoms : 6,840,534
Num. Elements : 16
V_PRM2019
Description :
This dataset was designed to enable machine-learning of V elastic, thermal, and defect properties, as well as surface energetics, melting, and the str...
Authors :
Elements :
V
Source Data :
https://gitlab.com/acclab/gap-data/-/tree/master
Source Pub. :
https://doi.org/10.1103/PhysRevMaterials.4.093802
Num. Configurations : 3,802
Num. Atoms : 46,466
Num. Elements : 1
W-14
Description :
158,000 diverse atomic environments of elemental tungsten.Includes DFT-PBE energies, forces and stresses for tungsten; periodic unit cells in the rang...
Authors :
Elements :
W
Source Data :
https://qmml.org/datasets.html
Source Pub. :
https://doi.org/10.1103/PhysRevB.90.104108
Num. Configurations : 9,693
Num. Atoms : 158,515
Num. Elements : 1
WBe_PRB2019
Description :
This data set was originally used to generate a multi-component linear SNAP potential for tungsten and beryllium as published in Wood, M. A., et. al. ...
Authors :
Elements :
Be, W
Source Pub. :
https://doi.org/10.1103/PhysRevB.99.184305
Num. Configurations : 25,120
Num. Atoms : 525,915
Num. Elements : 2
WS22_acrolein
Description :
Configurations of acrolein from WS22. The WS22 database combines Wigner sampling with geometry interpolation to generate 1.18 million molecular geomet...
Authors :
Elements :
C, H, O
Source Data :
https://doi.org/10.5281/zenodo.7032333
Source Pub. :
https://doi.org/10.1038/s41597-023-01998-3
Num. Configurations : 120,000
Num. Atoms : 960,000
Num. Elements : 3
WS22_alanine
Description :
Configurations of alanine from WS22. The WS22 database combines Wigner sampling with geometry interpolation to generate 1.18 million molecular geometr...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.5281/zenodo.7032333
Source Pub. :
https://doi.org/10.1038/s41597-023-01998-3
Num. Configurations : 120,000
Num. Atoms : 1,560,000
Num. Elements : 4
WS22_dmabn
Description :
Configurations of dmabn from WS22. The WS22 database combines Wigner sampling with geometry interpolation to generate 1.18 million molecular geometrie...
Authors :
Elements :
C, H, N
Source Data :
https://doi.org/10.5281/zenodo.7032333
Source Pub. :
https://doi.org/10.1038/s41597-023-01998-3
Num. Configurations : 120,000
Num. Atoms : 2,520,000
Num. Elements : 3
WS22_nitrophenol
Description :
Configurations of nitrophenol from WS22. The WS22 database combines Wigner sampling with geometry interpolation to generate 1.18 million molecular geo...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.5281/zenodo.7032333
Source Pub. :
https://doi.org/10.1038/s41597-023-01998-3
Num. Configurations : 120,000
Num. Atoms : 1,800,000
Num. Elements : 4
WS22_o-hbdi
Description :
Configurations of o-hbdi from WS22. The WS22 database combines Wigner sampling with geometry interpolation to generate 1.18 million molecular geometri...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.5281/zenodo.7032333
Source Pub. :
https://doi.org/10.1038/s41597-023-01998-3
Num. Configurations : 120,000
Num. Atoms : 2,640,000
Num. Elements : 4
WS22_sma
Description :
Configurations of sma from WS22. The WS22 database combines Wigner sampling with geometry interpolation to generate 1.18 million molecular geometries ...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.5281/zenodo.7032333
Source Pub. :
https://doi.org/10.1038/s41597-023-01998-3
Num. Configurations : 120,040
Num. Atoms : 2,280,760
Num. Elements : 4
WS22_thymine
Description :
Configurations of o-hbdi from WS22. The WS22 database combines Wigner sampling with geometry interpolation to generate 1.18 million molecular geometri...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.5281/zenodo.7032333
Source Pub. :
https://doi.org/10.1038/s41597-023-01998-3
Num. Configurations : 120,000
Num. Atoms : 1,800,000
Num. Elements : 4
WS22_toluene
Description :
Configurations of toluene from WS22. The WS22 database combines Wigner sampling with geometry interpolation to generate 1.18 million molecular geometr...
Authors :
Elements :
C, H
Source Data :
https://doi.org/10.5281/zenodo.7032333
Source Pub. :
https://doi.org/10.1038/s41597-023-01998-3
Num. Configurations : 100,000
Num. Atoms : 1,500,000
Num. Elements : 2
WS22_urea
Description :
Configurations of urea from WS22. The WS22 database combines Wigner sampling with geometry interpolation to generate 1.18 million molecular geometries...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.5281/zenodo.7032333
Source Pub. :
https://doi.org/10.1038/s41597-023-01998-3
Num. Configurations : 120,000
Num. Atoms : 960,000
Num. Elements : 4
WS22_urocanic
Description :
Configurations of urocanic from WS22. The WS22 database combines Wigner sampling with geometry interpolation to generate 1.18 million molecular geomet...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.5281/zenodo.7032333
Source Pub. :
https://doi.org/10.1038/s41597-023-01998-3
Num. Configurations : 120,000
Num. Atoms : 1,920,000
Num. Elements : 4
W_LML-retrain_bulk_MD_test
Description :
Test set from W_LML-retrain dataset, containing bulk tungsten calculations. The W_LML-retrain dataset contains DFT calculations used in testing a line...
Authors :
Elements :
W
Source Data :
https://github.com/marseille-matmol/LML-retrain
Source Pub. :
https://doi.org/10.1016/j.actamat.2023.118734
Num. Configurations : 8
Num. Atoms : 1,996
Num. Elements : 1
W_PRB2019
Description :
This dataset was originally designed to fit a GAP potential with a specific focus on properties relevant for simulations of radiation-induced collisio...
Authors :
Elements :
W
Source Pub. :
https://doi.org/10.1103/PhysRevB.100.144105
Num. Configurations : 3,528
Num. Atoms : 42,068
Num. Elements : 1
Yttrium-catalyzed_benzylic_C-H_alkylations_of_alkylpyridines_with_olefins
Description :
This data was assembled to investigate rare-earth-catalyzed benzylic C(sp3)-H addition of pyridines to olefins. All calculations were performed with t...
Authors :
Elements :
C, H, N, Y
Source Data :
https://doi.org/10.1021/acs.organomet.8b00397.s002
Source Pub. :
https://doi.org/10.1021/acs.organomet.8b00397
Num. Configurations : 68
Num. Atoms : 4,110
Num. Elements : 4
ZIF-4_Amorphous_Zeolitic_Imidazolate_Frameworks_2023
Description :
This dataset contains four trajectories of amorphous zeolitic imidazolate frameworks (ZIF-4), liquids calculated at four different volumes and at tem...
Authors :
Elements :
C, H, N, Zn
Source Data :
https://doi.org/10.5281/zenodo.10015594
Source Pub. :
https://doi.org/10.26434/chemrxiv-2023-8003d
Num. Configurations : 1,189,836
Num. Atoms : 323,635,392
Num. Elements : 4
Zeo-1_SD_2022
Description :
130,000 configurations of zeolite from the Database of Zeolite Structures. Calculations performed using Amsterdam Modeling Suite software.
Authors :
Elements :
Al, Ba, Be, C, Ca, Cs, F, Ge, H, K, Li, N, Na, O, Si
Source Data :
https://doi.org/10.1038/s41597-022-01160-5
Source Pub. :
https://doi.org/10.24435/materialscloud:cv-zd
Num. Configurations : 12,930
Num. Atoms : 1,841,742
Num. Elements : 15
Zn_MTP_CMS2023
Description :
A training dataset of diverse atomic configurations of Zn, varying in aggregation states, crystal structures, defect types, and sizes. The aim was to ...
Authors :
Elements :
Zn
Source Data :
https://github.com/meihaojie/Zn_system/tree/main
Source Pub. :
https://doi.org/10.1016/j.commatsci.2023.112723
Num. Configurations : 13,552
Num. Atoms : 278,996
Num. Elements : 1
Zr_Sn_JNM_2024
Description :
This dataset contains data from density functional theory calculations of various atomic configurations of pure Zr, pure Sn, and Zr-Sn alloys with dif...
Authors :
Elements :
Sn, Zr
Source Data :
https://github.com/meihaojie/Zr_Sn_system
Source Pub. :
https://doi.org/10.1016/j.jnucmat.2023.154794
Num. Configurations : 23,611
Num. Atoms : 688,087
Num. Elements : 2
a-AlOx_JCP_2020
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 an...
Authors :
Elements :
Al, O
Source Data :
https://doi.org/10.24435/materialscloud:y1-zd
Source Pub. :
https://doi.org/10.1063/5.0026289
Num. Configurations : 123,586
Num. Atoms : 4,541,918
Num. Elements : 2
aC_JCP_2023
Description :
The amorphous carbon dataset was generated using ab initio calculations with VASP software. We utilized the LDA exchange-correlation functional and th...
Authors :
Elements :
C
Source Data :
https://doi.org/10.5281/zenodo.7905585
Source Pub. :
https://doi.org/10.1063/5.0159349
Num. Configurations : 20,195
Num. Atoms : 5,192,400
Num. Elements : 1
aC_JCP_2023_test
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 wit...
Authors :
Elements :
C
Source Data :
https://doi.org/10.5281/zenodo.7905585
Source Pub. :
https://doi.org/10.1063/5.0159349
Num. Configurations : 3,366
Num. Atoms : 727,056
Num. Elements : 1
aC_JCP_2023_train
Description :
Train split from the 216-atom amorphous portion of the aC_JCP_2023 dataset. The amorphous carbon dataset was generated using ab initio calculations wi...
Authors :
Elements :
C
Source Data :
https://doi.org/10.5281/zenodo.7905585
Source Pub. :
https://doi.org/10.1063/5.0159349
Num. Configurations : 13,464
Num. Atoms : 2,908,224
Num. Elements : 1
adatoms_on_single-layer_graphene_PRR2021
Description :
This dataset consists of graphene superlattices with tungsten adatoms with properties calculated at the DFT level of theory. The authors modeled the p...
Authors :
Elements :
C, Cr, Ir, Mo, Nb, Os, Re, Rh, Ru, Ta, W
Source Data :
https://doi.org/10.24435/materialscloud:bj-bh
Source Pub. :
http://doi.org/10.1103/PhysRevResearch.3.L032003
Num. Configurations : 18
Num. Atoms : 774
Num. Elements : 11
aleatoric_epistemic_error_AIC2023
Description :
Dataset for H2CO, with and without added noise for testing the effects of noise on quality of fit. Configurations sets are included for clean energy v...
Authors :
Elements :
C, H, O
Source Data :
https://github.com/MMunibas/noise
Source Pub. :
https://doi.org/10.1016/j.aichem.2023.100033
Num. Configurations : 28,808
Num. Atoms : 115,232
Num. Elements : 3
alkali-metal_intercalation_in_disordered_carbon_anode_materials_JMCA2019
Description :
A dataset created as part of a combination DFT-ML approach to study three alkali metals (K, Li, Na) in model carbon systems at a range of densities an...
Authors :
Elements :
C, K, Li, Na
Source Data :
https://doi.org/10.17863/CAM.42087
Source Pub. :
https://doi.org/10.1039/C9TA05453G
Num. Configurations : 1,365
Num. Atoms : 298,050
Num. Elements : 4
alpha_brass_nanoparticles
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.
Authors :
Elements :
Cu, Zn
Source Data :
https://doi.org/10.24435/materialscloud:94-aq
Source Pub. :
https://doi.org/10.1021/acs.jpcc.1c02314
Num. Configurations : 53,696
Num. Atoms : 2,956,679
Num. Elements : 2
cG-SchNet
Description :
Configurations from a cG-SchNet trained on a subset of the QM9dataset. Model was trained with the intention of providing molecules withspecified funct...
Authors :
Elements :
C, F, H, N, O
Source Data :
https://github.com/atomistic-machine-learning/cG-SchNet/
Source Pub. :
https://doi.org/10.1038/s41467-022-28526-y
Num. Configurations : 79,772
Num. Atoms : 1,467,492
Num. Elements : 5
calcium_ferrites_as_cathodes_ca4fe9o17
Description :
Dataset for "Appraisal of calcium ferrites as cathodes for calcium rechargeable batteries: DFT, synthesis, characterization and electrochemistry of Ca...
Authors :
Elements :
Ca, Fe, O
Source Data :
https://doi.org/10.24435/materialscloud:xk-sn
Source Pub. :
http://doi.org/10.1039/c9dt04688g
Num. Configurations : 345
Num. Atoms : 35,462
Num. Elements : 3
cathode_materials_for_rechargeable_Ca_batteries_CM2021
Description :
Data from the publication "Enlisting Potential Cathode Materials for Rechargeable Ca Batteries". The development of rechargeable batteries based on a ...
Authors :
Elements :
Ca, Co, Fe, Mn, N, Ni, O, P, S, Si, V
Source Data :
https://doi.org/10.24435/materialscloud:3n-e8
Source Pub. :
http://doi.org/10.1038/s41598-019-46002-4
Num. Configurations : 10,840
Num. Atoms : 1,034,770
Num. Elements : 11
datasets_for_magnetic_MTP_NatSR2024_training
Description :
This dataset comprises a training dataset for magnetic multi-component machine-learning potentials for Fe-Al systems, including different concentratio...
Authors :
Elements :
Al, Fe
Source Data :
https://gitlab.com/ivannovikov/datasets_for_magnetic_MTP
Source Pub. :
https://doi.org/10.1038/s41598-023-46951-x
Num. Configurations : 2,012
Num. Atoms : 11,440
Num. Elements : 2
datasets_for_magnetic_MTP_NatSR2024_verification
Description :
This is the verification dataset (see companion training dataset: datasets_for_magnetic_MTP_NatSR2024_training) used in training a magnetic multi-comp...
Authors :
Elements :
Al, Fe
Source Data :
https://gitlab.com/ivannovikov/datasets_for_magnetic_MTP
Source Pub. :
https://doi.org/10.1038/s41598-023-46951-x
Num. Configurations : 336
Num. Atoms : 3,696
Num. Elements : 2
defected_phosphorene_ACS_2023
Description :
This dataset contains pristine monolayer phosphorene as well as structures with monovacancies which were used to train an artificial neural network (A...
Authors :
Elements :
P
Source Data :
https://doi.org/10.5281/zenodo.8421094
Source Pub. :
https://doi.org/10.1021/acs.jpcc.3c05713
Num. Configurations : 5,091
Num. Atoms : 722,311
Num. Elements : 1
discrepencies_and_error_metrics_NPJ_2023_enhanced_validation_set
Description :
Structures from discrepencies_and_error_metrics_NPJ_2023 validation set, enhanced by inclusion of rare events. The full discrepencies_and_error_metric...
Authors :
Elements :
Si
Source Data :
https://github.com/mogroupumd/Silicon_MLIP_datasets
Source Pub. :
https://doi.org/10.1038/s41524-023-01123-3
Num. Configurations : 50
Num. Atoms : 3,198
Num. Elements : 1
discrepencies_and_error_metrics_NPJ_2023_interstitial_enhanced_training_set
Description :
Structures from discrepencies_and_error_metrics_NPJ_2023 training set, enhanced by inclusion of interstitials. The full discrepencies_and_error_metric...
Authors :
Elements :
Si
Source Data :
https://github.com/mogroupumd/Silicon_MLIP_datasets
Source Pub. :
https://doi.org/10.1038/s41524-023-01123-3
Num. Configurations : 218
Num. Atoms : 13,629
Num. Elements : 1
discrepencies_and_error_metrics_NPJ_2023_interstitial_re_testing_set
Description :
Structures from discrepencies_and_error_metrics_NPJ_2023 test set; these include an interstitial. The full discrepencies_and_error_metrics_NPJ_2023 da...
Authors :
Elements :
Si
Source Data :
https://github.com/mogroupumd/Silicon_MLIP_datasets
Source Pub. :
https://doi.org/10.1038/s41524-023-01123-3
Num. Configurations : 100
Num. Atoms : 6,500
Num. Elements : 1
discrepencies_and_error_metrics_NPJ_2023_vacancy_enhanced_training_set
Description :
Structures from discrepencies_and_error_metrics_NPJ_2023 training set; includes some structures with vacancies. The full discrepencies_and_error_metri...
Authors :
Elements :
Si
Source Data :
https://github.com/mogroupumd/Silicon_MLIP_datasets
Source Pub. :
https://doi.org/10.1038/s41524-023-01123-3
Num. Configurations : 218
Num. Atoms : 13,389
Num. Elements : 1
discrepencies_and_error_metrics_NPJ_2023_vacancy_re_testing_set
Description :
Structures from discrepencies_and_error_metrics_NPJ_2023 test set; these include a single migrating vacancy. The full discrepencies_and_error_metrics_...
Authors :
Elements :
Si
Source Data :
https://github.com/mogroupumd/Silicon_MLIP_datasets
Source Pub. :
https://doi.org/10.1038/s41524-023-01123-3
Num. Configurations : 100
Num. Atoms : 6,300
Num. Elements : 1
disordered_transition_metal_oxyfluorides_EA2021
Description :
Data from "On-the-fly assessment of diffusion barriers of disordered transition metal oxyfluorides using local descriptors". The dataset contains the ...
Authors :
Elements :
F, Li, O, V
Source Data :
https://doi.org/10.24435/materialscloud:9v-3q
Source Pub. :
http://doi.org/10.1016/j.electacta.2021.138551
Num. Configurations : 233
Num. Atoms : 20,670
Num. Elements : 4
doped_CsPbI3_energetics_test
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 sta...
Authors :
Elements :
Cd, Cs, I, Pb, Zn
Source Pub. :
https://doi.org/10.1016/j.commatsci.2023.112672
Num. Configurations : 60
Num. Atoms : 9,600
Num. Elements : 5
doped_CsPbI3_energetics_train_validate
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 t...
Authors :
Elements :
Cd, Cs, I, Pb, Zn
Source Pub. :
https://doi.org/10.1016/j.commatsci.2023.112672
Num. Configurations : 142
Num. Atoms : 22,720
Num. Elements : 5
electrode_materials_for_ca-based_rechargeable_batteries
Description :
Dataset for "Analysis of minerals as electrode materials for Ca-based rechargeable batteries". Includes DFT structures of pyroxenes, garnet and carbon...
Authors :
Elements :
C, Ca, Cr, Mn, O, Si
Source Data :
https://doi.org/10.24435/materialscloud:3n-e8
Source Pub. :
http://doi.org/10.1038/s41598-019-46002-4
Num. Configurations : 4,726
Num. Atoms : 550,074
Num. Elements : 6
ferroelectricity_and_metallicity_in_BaTiO3_JMCC2021
Description :
Dataset for "Interplay between ferroelectricity and metallicity in BaTiO3", exploring properties of ferroelectric barium titanate (BaTiO3), including ...
Authors :
Elements :
Al, Ba, K, La, Nb, O, Sc, Ti, V
Source Data :
https://doi.org/10.24435/materialscloud:f4-94
Source Pub. :
http://doi.org/10.1039/D1TC01868J
Num. Configurations : 1,127
Num. Atoms : 19,125
Num. Elements : 9
flexible_molecules_JCP2021
Description :
Configurations of azobenzene featuring a cis to trans thermal inversion through three channels: inversion, rotation, and rotation assisted by inversio...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.1063/5.0038516
Source Pub. :
https://doi.org/10.1063/5.0038516
Num. Configurations : 69,182
Num. Atoms : 1,520,340
Num. Elements : 4
glass-ceramic_lithium_thiophosphate_electrolytes_
Description :
This database contains computationally generated atomic structures of glass-ceramics lithium thiophosphates (gc-LPS) with the general composition (Li2...
Authors :
Elements :
Li, P, S
Source Data :
https://doi.org/10.24435/materialscloud:j5-tz
Source Pub. :
https://doi.org/10.1021/acs.chemmater.2c00267
Num. Configurations : 6,055
Num. Atoms : 264,604
Num. Elements : 3
linear_magnetic_coefficient_in_Cr2O3_JPCM2024
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 ...
Authors :
Elements :
Cr, O
Source Data :
https://doi.org/10.24435/materialscloud:ek-fp
Source Pub. :
http://doi.org/10.1088/1361-648X/ad1a59
Num. Configurations : 165
Num. Atoms : 1,650
Num. Elements : 2
local_polarization_in_oxygen-deficient_LaMnO3_PRR2020
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 ...
Authors :
Elements :
Ba, La, Mn, O, Ti
Source Data :
https://doi.org/10.24435/materialscloud:m9-9d
Source Pub. :
http://doi.org/10.1103/PhysRevResearch.2.042040
Num. Configurations : 4,514
Num. Atoms : 174,337
Num. Elements : 5
mbGDML_maldonado_2023
Description :
Configurations of water, acetonitrile and methanol, simulated with ASE and modeled using a variety of software and methods: GAP, SchNet, GDML, ORCA an...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.5281/zenodo.7112197
Source Pub. :
https://doi.org/10.26434/chemrxiv-2023-wdd1r
Num. Configurations : 24,543
Num. Atoms : 712,134
Num. Elements : 4
mlearn_Cu_test
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 ...
Authors :
Elements :
Cu
Source Data :
https://github.com/materialsvirtuallab/mlearn
Source Pub. :
https://doi.org/10.1021/acs.jpca.9b08723
Num. Configurations : 31
Num. Atoms : 3,178
Num. Elements : 1
mlearn_Cu_train
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 ...
Authors :
Elements :
Cu
Source Pub. :
https://doi.org/10.1021/acs.jpca.9b08723
Num. Configurations : 262
Num. Atoms : 27,416
Num. Elements : 1
mlearn_Ge_test
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 ...
Authors :
Elements :
Ge
Source Data :
https://github.com/materialsvirtuallab/mlearn
Source Pub. :
https://doi.org/10.1021/acs.jpca.9b08723
Num. Configurations : 25
Num. Atoms : 1,568
Num. Elements : 1
mlearn_Ge_train
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 ...
Authors :
Elements :
Ge
Source Pub. :
https://doi.org/10.1021/acs.jpca.9b08723
Num. Configurations : 228
Num. Atoms : 14,072
Num. Elements : 1
mlearn_Li_test
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 ...
Authors :
Elements :
Li
Source Data :
https://github.com/materialsvirtuallab/mlearn
Source Pub. :
https://doi.org/10.1021/acs.jpca.9b08723
Num. Configurations : 29
Num. Atoms : 1,320
Num. Elements : 1
mlearn_Li_train
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 ...
Authors :
Elements :
Li
Source Pub. :
https://doi.org/10.1021/acs.jpca.9b08723
Num. Configurations : 241
Num. Atoms : 11,576
Num. Elements : 1
mlearn_Mo_test
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 ...
Authors :
Elements :
Mo
Source Data :
https://github.com/materialsvirtuallab/mlearn
Source Pub. :
https://doi.org/10.1021/acs.jpca.9b08723
Num. Configurations : 23
Num. Atoms : 1,189
Num. Elements : 1
mlearn_Mo_train
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 ...
Authors :
Elements :
Mo
Source Pub. :
https://doi.org/10.1021/acs.jpca.9b08723
Num. Configurations : 194
Num. Atoms : 10,087
Num. Elements : 1
mlearn_Ni_test
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 ...
Authors :
Elements :
Ni
Source Data :
https://github.com/materialsvirtuallab/mlearn
Source Pub. :
https://doi.org/10.1021/acs.jpca.9b08723
Num. Configurations : 31
Num. Atoms : 3,158
Num. Elements : 1
mlearn_Ni_train
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 ...
Authors :
Elements :
Ni
Source Pub. :
https://doi.org/10.1021/acs.jpca.9b08723
Num. Configurations : 263
Num. Atoms : 27,420
Num. Elements : 1
mlearn_Si_test
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 ...
Authors :
Elements :
Si
Source Data :
https://github.com/materialsvirtuallab/mlearn
Source Pub. :
https://doi.org/10.1021/acs.jpca.9b08723
Num. Configurations : 25
Num. Atoms : 1,525
Num. Elements : 1
mlearn_Si_train
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 ...
Authors :
Elements :
Si
Source Pub. :
https://doi.org/10.1021/acs.jpca.9b08723
Num. Configurations : 214
Num. Atoms : 13,233
Num. Elements : 1
oxygen-vacancy_defects_in_Cu2O(111)
Description :
This dataset investigates the effect of defects, such as copper and oxygen vacancies, in cuprous oxide films. Structures include oxygen vacancies form...
Authors :
Elements :
Cu, O
Source Data :
https://doi.org/10.24435/materialscloud:3z-bk
Source Pub. :
http://doi.org/10.1088/2516-1075/ace0aa
Num. Configurations : 864
Num. Atoms : 606,270
Num. Elements : 2
pure_magnesium_DFT_PRM2020
Description :
This dataset provides DFT (as implemented in VASP) calculations for pure magnesium. Configuration sets include bulk, generalized stacking fault energi...
Authors :
Elements :
Mg
Source Data :
https://doi.org/10.24435/materialscloud:8f-1s
Source Pub. :
https://doi.org/10.1103/PhysRevMaterials.4.103602
Num. Configurations : 405
Num. Atoms : 10,730
Num. Elements : 1
q-AQUA
Description :
The a-AQUA dataset was generated to address the need for a training set for a water PES that includes 2-body, 3-body and 4-body interactions calculate...
Authors :
Elements :
H, O
Source Data :
https://github.com/jmbowma/q-AQUA
Source Pub. :
https://doi.org/10.1021/acs.jpclett.2c00966
Num. Configurations : 120,372
Num. Atoms : 878,157
Num. Elements : 2
rMD17
Description :
A dataset of 10 molecules (aspirin, azobenzene, benzene, ethanol, malonaldehyde, naphthalene, paracetamol, salicylic, toluene, uracil) with 100,000 st...
Authors :
Elements :
C, H, N, O
Source Data :
https://doi.org/10.6084/m9.figshare.12672038.v3
Source Pub. :
https://doi.org/10.48550/arXiv.2007.09593
Num. Configurations : 999,988
Num. Atoms : 15,599,712
Num. Elements : 4
reactive_hydrogen_ACS_2023
Description :
This dataset contains structures of Cu, including Cu(111), Cu(100), Cu(110), and Cu(211). Slab settings are as follows: 3 x 3, 6-layered slabs for Cu(...
Authors :
Elements :
Cu, H
Source Data :
https://dx.doi.org/10.17172/NOMAD/2023.05.03-2
Source Pub. :
https://pubs.acs.org/doi/full/10.1021/acs.jpcc.3c06648
Num. Configurations : 3,413
Num. Atoms : 191,104
Num. Elements : 2
reduced-perovskite_and_oxidized-marokite_oxides
Description :
Dataset contains DFT calculations of oxygen-deficient perovskites from the Ca2Fe2O5-brownmillerite and Ca2Mn2O5 structures; and tunnel CaMn4O8, a deri...
Authors :
Elements :
Ca, Fe, Mn, O
Source Data :
https://doi.org/10.24435/materialscloud:x9-qr
Source Pub. :
http://doi.org/10.1016/j.ensm.2019.06.002
Num. Configurations : 2,919
Num. Atoms : 387,258
Num. Elements : 4
sAlex_train
Description :
The training split of sAlex. sAlex is a subsample of the Alexandria dataset that was used to fine tune the OMat24 (Open Materials 2024) models. From t...
Authors :
Elements :
Ac, Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 10,345,613
Num. Atoms : 106,888,622
Num. Elements : 89
sAlex_validation
Description :
The validation split of sAlex. sAlex is a subsample of the Alexandria dataset that was used to fine tune the OMat24 (Open Materials 2024) models. From...
Authors :
Elements :
Ac, Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl...
Source Data :
https://fair-chem.github.io/core/datasets/omat24.html
Source Pub. :
https://doi.org/10.48550/arXiv.2410.12771
Num. Configurations : 547,885
Num. Atoms : 5,670,890
Num. Elements : 86
sGDML_Aspirin_ccsd_NC2018_test
Description :
The test set of a train/test pair from the aspirin dataset from sGDML. To create the coupled cluster datasets, the data used for training the models w...
Authors :
Elements :
C, H, O
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1038/s41467-018-06169-2
Num. Configurations : 500
Num. Atoms : 10,500
Num. Elements : 3
sGDML_Aspirin_ccsd_NC2018_train
Description :
The train set of a train/test pair from the aspirin dataset from sGDML. To create the coupled cluster datasets, the data used for training the models ...
Authors :
Elements :
C, H, O
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1038/s41467-018-06169-2
Num. Configurations : 1,000
Num. Atoms : 21,000
Num. Elements : 3
sGDML_Benzene_DFT_NC2018
Description :
The data used for training the DFT models were created running ab initio MD in the NVT ensemble using the Nosé-Hoover thermostat at 500 K during a 200...
Authors :
Elements :
C, H
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1126/sciadv.1603015
Num. Configurations : 49,863
Num. Atoms : 598,356
Num. Elements : 2
sGDML_Benzene_ccsdt_NC2018_test
Description :
The test set of a train/test pair from the benzene dataset from sGDML. To create the coupled cluster datasets, the data used for training the models w...
Authors :
Elements :
C, H
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1038/s41467-018-06169-2
Num. Configurations : 500
Num. Atoms : 6,000
Num. Elements : 2
sGDML_Benzene_ccsdt_NC2018_train
Description :
The train set of a train/test pair from the benzene dataset from sGDML. To create the coupled cluster datasets, the data used for training the models ...
Authors :
Elements :
C, H
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1038/s41467-018-06169-2
Num. Configurations : 1,000
Num. Atoms : 12,000
Num. Elements : 2
sGDML_Ethanol_ccsdt_NC2018_test
Description :
The test set of a train/test pair from the ethanol dataset from sGDML. To create the coupled cluster datasets, the data used for training the models w...
Authors :
Elements :
C, H, O
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1038/s41467-018-06169-2
Num. Configurations : 1,000
Num. Atoms : 9,000
Num. Elements : 3
sGDML_Ethanol_ccsdt_NC2018_train
Description :
The train set of a train/test pair from the ethanol dataset from sGDML. To create the coupled cluster datasets, the data used for training the models ...
Authors :
Elements :
C, H, O
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1038/s41467-018-06169-2
Num. Configurations : 998
Num. Atoms : 8,982
Num. Elements : 3
sGDML_Malonaldehyde_ccsdt_NC2018_test
Description :
The test set of a train/test pair from the malonaldehyde dataset from sGDML. To create the coupled cluster datasets, the data used for training the mo...
Authors :
Elements :
C, H, O
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1038/s41467-018-06169-2
Num. Configurations : 500
Num. Atoms : 4,500
Num. Elements : 3
sGDML_Malonaldehyde_ccsdt_NC2018_train
Description :
The train set of a train/test pair from the malonaldehyde dataset from sGDML. To create the coupled cluster datasets, the data used for training the m...
Authors :
Elements :
C, H, O
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1038/s41467-018-06169-2
Num. Configurations : 1,000
Num. Atoms : 9,000
Num. Elements : 3
sGDML_Toluene_ccsdt_NC2018_test
Description :
The test set of a train/test pair from the toluene dataset from sGDML. To create the coupled cluster datasets, the data used for training the models w...
Authors :
Elements :
C, H
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1038/s41467-018-06169-2
Num. Configurations : 501
Num. Atoms : 7,515
Num. Elements : 2
sGDML_Toluene_ccsdt_NC2018_train
Description :
The train set of a train/test pair from the toluene dataset from sGDML. To create the coupled cluster datasets, the data used for training the models ...
Authors :
Elements :
C, H
Source Data :
http://sgdml.org/
Source Pub. :
https://doi.org/10.1038/s41467-018-06169-2
Num. Configurations : 1,000
Num. Atoms : 15,000
Num. Elements : 2
solute_strengthening_of_prism_edge_locations_in_Mg_alloys
Description :
This dataset includes Mg and Mg-Zn alloy structures with solute atoms at the prism edge locations. The dataset was created to study the strengthening ...
Authors :
Elements :
Mg, Zn
Source Data :
https://doi.org/10.24435/materialscloud:1e-c7
Source Pub. :
http://doi.org/10.1016/j.euromechsol.2023.105128
Num. Configurations : 94
Num. Atoms : 28,615
Num. Elements : 2
solvated_protein_fragments_JCTC_2019
Description :
The solvated protein fragments dataset was generated as a partner benchmark dataset, along with SN2, for measuring the performance of machine learning...
Authors :
Elements :
C, H, N, O, S
Source Data :
https://doi.org/10.5281/zenodo.2605372
Source Pub. :
https://doi.org/10.1021/acs.jctc.9b00181
Num. Configurations : 2,731,180
Num. Atoms : 58,395,272
Num. Elements : 5
stable_and_metastable_phases_in_sputtered_CuInS2
Description :
The chalcopyrite Cu(In,Ga)S2 has gained renewed interest in recent years due to its potential application in tandem solar cells. In this contribution,...
Authors :
Elements :
Cu, In, Na, S
Source Data :
https://doi.org/10.24435/materialscloud:5n-1e
Source Pub. :
http://doi.org/https://doi.org/10.1002/advs.202200848
Num. Configurations : 3,105
Num. Atoms : 117,948
Num. Elements : 4
tmQM_wB97MV
Description :
tmQM_wB97MV contains configurations from the tmQM dataset, with several structures from tmQM that were found to be missing hydrogens filtered out, and...
Authors :
Elements :
Ag, As, Au, B, Br, C, Cd, Cl, Co, Cr, Cu, F, Fe, H, Hf, ...
Source Data :
https://github.com/ulissigroup/tmQM_wB97MV
Source Pub. :
https://doi.org/10.1021/acs.jcim.3c01226
Num. Configurations : 86,507
Num. Atoms : 5,710,877
Num. Elements : 44
vanadium_in_high_entropy_alloys_AM2020
Description :
Dataset created for "Vanadium is an optimal element for strengthening in both fcc and bcc high-entropy alloys", to explore the effect of V in the high...
Authors :
Elements :
Ni, V
Source Data :
https://doi.org/10.24435/materialscloud:2020.0020/v1
Source Pub. :
http://doi.org/10.1016/j.actamat.2020.01.062
Num. Configurations : 261
Num. Atoms : 22,196
Num. Elements : 2
water_and_Cu+_synergy_in_selective_CO2_hydrogenation_to_methanol_over_Cu/MgO_catalysts
Description :
This dataset was created to investigate the role of surface water and hydroxyl groups in facilitating spontaneous CO₂ activation at Cu⁺ sites and the ...
Authors :
Elements :
C, Cu, H, Mg, O
Source Data :
https://doi.org/10.24435/materialscloud:tz-pn
Source Pub. :
https://doi.org/10.1021/jacs.3c10685
Num. Configurations : 14,962
Num. Atoms : 1,043,709
Num. Elements : 5
water_ice_JCP_2020
Description :
Starting from a single reference ab initio simulation, we use active learning to expand into new state points and to describe the quantum nature of th...
Authors :
Elements :
H, O
Source Data :
https://doi.org/10.5281/zenodo.4004590
Source Pub. :
https://doi.org/10.1063/5.0016004
Num. Configurations : 8,000
Num. Atoms : 2,088,000
Num. Elements : 2
water_ice_NEP_2023
Description :
The main part of the dataset consists of structures of liquid water at 300 K from first-principles molecular dynamics (FPMD) simulations using a hybri...
Authors :
Elements :
H, O
Source Data :
https://github.com/ZKC19940412/water_ice_nep
Source Pub. :
https://doi.org/10.26434/chemrxiv-2023-sr496
Num. Configurations : 814
Num. Atoms : 216,144
Num. Elements : 2
water_ice_PNAS_2021
Description :
Dataset generated using a committee-based active learning strategy to build a training dataset for modeling complex aqueous systems.
Authors :
Elements :
B, C, F, H, Mo, N, O, S, Ti
Source Data :
https://doi.org/10.5281/zenodo.5235246
Source Pub. :
https://doi.org/10.1073/pnas.2110077118
Num. Configurations : 1,786
Num. Atoms : 681,912
Num. Elements : 9
xxMD-CASSCF_test
Description :
Test dataset from xxMD-CASSCF. The xxMD (Extended Excited-state Molecular Dynamics) dataset is a comprehensive collection of non-adiabatic trajectorie...
Authors :
Elements :
C, H, N, O, S
Source Data :
https://github.com/zpengmei/xxMD
Source Pub. :
https://doi.org/10.48550/arXiv.2308.11155
Num. Configurations : 65,100
Num. Atoms : 707,552
Num. Elements : 5
xxMD-CASSCF_train
Description :
Training dataset from xxMD-CASSCF. The xxMD (Extended Excited-state Molecular Dynamics) dataset is a comprehensive collection of non-adiabatic traject...
Authors :
Elements :
C, H, N, O, S
Source Data :
https://github.com/zpengmei/xxMD
Source Pub. :
https://doi.org/10.48550/arXiv.2308.11155
Num. Configurations : 130,179
Num. Atoms : 1,411,656
Num. Elements : 5
xxMD-CASSCF_validation
Description :
Validation dataset from xxMD-CASSCF. The xxMD (Extended Excited-state Molecular Dynamics) dataset is a comprehensive collection of non-adiabatic traje...
Authors :
Elements :
C, H, N, O, S
Source Data :
https://github.com/zpengmei/xxMD
Source Pub. :
https://doi.org/10.48550/arXiv.2308.11155
Num. Configurations : 64,848
Num. Atoms : 701,516
Num. Elements : 5
xxMD-DFT_test
Description :
Test dataset from xxMD-DFT. The xxMD (Extended Excited-state Molecular Dynamics) dataset is a comprehensive collection of non-adiabatic trajectories e...
Authors :
Elements :
C, H, N, O, S
Source Data :
https://github.com/zpengmei/xxMD
Source Pub. :
https://doi.org/10.48550/arXiv.2308.11155
Num. Configurations : 21,661
Num. Atoms : 402,856
Num. Elements : 5
xxMD-DFT_train
Description :
Training dataset from xxMD-DFT. The xxMD (Extended Excited-state Molecular Dynamics) dataset is a comprehensive collection of non-adiabatic trajectori...
Authors :
Elements :
C, H, N, O, S
Source Data :
https://github.com/zpengmei/xxMD
Source Pub. :
https://doi.org/10.48550/arXiv.2308.11155
Num. Configurations : 43,395
Num. Atoms : 807,416
Num. Elements : 5
xxMD-DFT_validation
Description :
Validation dataset from xxMD-DFT. The xxMD (Extended Excited-state Molecular Dynamics) dataset is a comprehensive collection of non-adiabatic trajecto...
Authors :
Elements :
C, H, N, O, S
Source Data :
https://github.com/zpengmei/xxMD
Source Pub. :
https://doi.org/10.48550/arXiv.2308.11155
Num. Configurations : 21,606
Num. Atoms : 402,151
Num. Elements : 5