Dataset

GST_GAP_22_main



Download Dataset XYZ file

Name GST_GAP_22_main
Extended ID GST_GAP_22_main_ZhouZhangMaDeringer__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.
Elements Ge (23.63%)
Sb (21.86%)
Te (54.51%)
Number of Data Objects 2,692
Number of Configurations 2,692
Number of Atoms 341,068
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)
Data Objects
ColabFit ID DS_r3hav37ufnmb_0
Files colabfitspec.json

No uploaded content is transferred in ownership from the original creators to ColabFit. All content is distributed under the license specified by its contributor who has stated that he or she has the authority to share it under the specified license.