Dataset
GST_GAP_22_main
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| Name | GST_GAP_22_main |
|---|---|
| Extended ID | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
| Description | The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. |
| Authors |
Yuxing Zhou Wei Zhang Evan Ma Volker L. Deringer |
| DOI |
10.60732/f2d6e02c
https://commons.datacite.org/doi.org/10.60732/f2d6e02c https://doi.datacite.org/dois/10.60732%2Ff2d6e02c https://doi.org/10.60732/f2d6e02c Cite as: Zhou, Y., Zhang, W., Ma, E., and Deringer, V. L. "GST GAP 22 main." ColabFit, 2023. https://doi.org/10.60732/f2d6e02c. For other citation formats, see the DataCite Fabrica page for this dataset. |
| Calculated Property Types |
atomic_forces cauchy_stress energy |
| Elements |
Ge (23.63%) Sb (21.86%) Te (54.51%) |
| Number of Configurations | 2,692 |
| Number of Atoms | 341,068 |
| Links |
https://doi.org/10.5281/zenodo.8208202 https://doi.org/10.1038/s41928-023-01030-x |
| Configuration Sets by Name | (None) |
| Configuration Sets by ID | (None) |
| Calculated Properties | |
| ColabFit ID | DS_r3hav37ufnmb_0 |
| Files | colabfitspec.json |
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