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
ColabFit ID :
DS_jy3ylaf48xg3_0
Files :
colabfitspec.json
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.
Num. Configurations :
2,690
Num. Atoms :
341,004
Downloads :
30
Calculated Property Types :
atomic_forces cauchy_stress energy
Elements :
Ge (23.63%) Sb (21.86%) Te (54.51%)
Methods :
DFT-PBE
Software :
CASTEP
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 :
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