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Name GST_GAP_22_refitted
Extended ID GST_GAP_22_refitted_ZhouZhangMaDeringer__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

Cite as: Zhou, Y., Zhang, W., Ma, E., and Deringer, V. L. "GST GAP 22 refitted." ColabFit, 2023.
For other citation formats, see the DataCite Fabrica page for this dataset.
Elements Ge (23.63%)
Sb (21.86%)
Te (54.51%)
Number of Data Objects 2,692
Number of Configurations 2,692
Number of Atoms 341,004
Configuration Sets by Name (None)
Configuration Sets by ID (None)
Data Objects
ColabFit ID DS_jy3ylaf48xg3_0
Files colabfitspec.json

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