Dataset
GST_GAP_22_refitted
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| Name | GST_GAP_22_refitted |
|---|---|
| Extended ID | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
| Description | The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. |
| Authors |
Yuxing Zhou Wei Zhang Evan Ma Volker L. Deringer |
| DOI |
10.60732/164f9a70
https://commons.datacite.org/doi.org/10.60732/164f9a70 https://doi.datacite.org/dois/10.60732%2F164f9a70 https://doi.org/10.60732/164f9a70 Cite as: Zhou, Y., Zhang, W., Ma, E., and Deringer, V. L. "GST GAP 22 refitted." ColabFit, 2023. https://doi.org/10.60732/164f9a70. 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 | 2,690 |
| Number of Atoms | 341,004 |
| Publication Link | https://doi.org/10.1038/s41928-023-01030-x |
| Data Source Link | https://doi.org/10.5281/zenodo.8208202 |
| Configuration Sets by Name | |
| Configuration Sets by ID | |
| ColabFit ID | DS_jy3ylaf48xg3_0 |
| Downloads | 12 |
| Files | colabfitspec.json |
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