Dataset
mlearn_Cu_train
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| Name | mlearn_Cu_train |
|---|---|
| Extended ID | mlearn_Cu_train__Zuo-Chen-Li-Deng-Chen-Behler-Csányi-Shapeev-Thompson-Wood-Ong__DS_3pv3hck35iy6_0 |
| Description | A comprehensive DFT data set was generated for six elements - Li, Mo, Ni, Cu, Si, and Ge. These elements were chosen to span a variety of chemistries (main group metal, transition metal, and semiconductor), crystal structures (bcc, fcc, and diamond) and bonding types (metallic and covalent). This dataset comprises only the Cu configurations |
| Authors |
Yunxing Zuo Chi Chen Xiangguo Li Zhi Deng Yiming Chen Jörg Behler Gábor Csányi Alexander V. Shapeev Aidan P. Thompson Mitchell A. Wood Shyue Ping Ong |
| DOI |
10.60732/49de06ae
https://commons.datacite.org/doi.org/10.60732/49de06ae https://doi.datacite.org/dois/10.60732%2F49de06ae https://doi.org/10.60732/49de06ae Cite as: Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Csányi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., and Ong, S. P. "mlearn Cu train." ColabFit, 2023. https://doi.org/10.60732/49de06ae. For other citation formats, see the DataCite Fabrica page for this dataset. |
| Calculated Property Types |
atomic_forces cauchy_stress energy |
| Elements |
Cu (100.0%) |
| Number of Configurations | 262 |
| Number of Atoms | 27,416 |
| Links |
https://github.com/materialsvirtuallab/mlearn/tree/master/data https://doi.org/10.1021/acs.jpca.9b08723 |
| Configuration Sets by Name |
Cu_surface — Slab structures up to a maximum Miller index of three, including (100), (110), (111), (210), (211), (310), (311), (320), (321), (322), (331), and (332), as obtained from the Crystalium database. Cu_strain — Strained structures constructed by applying strains of -10% to 10% at 2% intervals to the bulk supercell in six different modes. The supercells used are the 3 x 3 x 3, 3 x 3 x 3, and 2 x 2 x 2 of the conventional bcc, fcc, and diamond unit cells, respectively Cu_ground — Ground state structure Cu_vacancy — NVT AIMD simulations of the bulk supercells with a single vacancy performed at 300 K and 2.0x of the melting point of each element. The bulk supercells were heated from 0 K to the target temperatures and equilibrated for 20,000 time steps. A total of 40 snapshots were obtained from the subsequent production run of each AIMD simulation at an interval of 0.1 ps. Cu_AIMD_NVT — NVT ab initio molecular dynamics (AIMD) simulations of the bulk supercells performed at 300 K and 0.5x, 0.9x, 1.5x, and 2.0x of the melting point of each element with a time step of 2 fs. The bulk supercells were heated from 0 K to the target temperatures and equilibrated for 20 000 time steps. A total of 20 snapshots were obtained from the subsequent production run in each AIMD simulation at an interval of 0.1 ps. |
| Configuration Sets by ID |
CS_61a6nx0fbsup_0 CS_dq50yvtpka7b_0 CS_htu42cpzhuk6_0 CS_lembrxja2gjo_0 CS_m4x8sbj9n88g_0 |
| Calculated Properties | |
| ColabFit ID | DS_3pv3hck35iy6_0 |
| Files | colabfitspec.json |
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