Datasets:

colabfit
/

GST_GAP_22_extended

configs:
  - config_name: default
    data_files: co/*.parquet
  - config_name: info
    data_files: ds.parquet
license: cc-by-4.0
tags:
  - molecular dynamics
  - mlip
  - interatomic potential
pretty_name: GST GAP 22 extended

Cite this dataset
Zhou, Y., Zhang, W., Ma, E., and Deringer, V. L. GST GAP 22 extended. ColabFit, 2023. https://doi.org/10.60732/37c76fa8

This dataset has been curated and formatted for the ColabFit Exchange

This dataset is also available on the ColabFit Exchange:

https://materials.colabfit.org/id/DS_efcq7a0h6z5w_0

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Dataset Name

GST GAP 22 extended

Description

The extended training dataset for GST_GAP_22, calculated using the PBEsol functional. New configurations, simulated under external electric fields, were labelled with DFT and added to the original reference database 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.

Dataset authors

Yuxing Zhou, Wei Zhang, Evan Ma, Volker L. Deringer

Publication

https://doi.org/10.1038/s41928-023-01030-x

Original data link

https://doi.org/10.5281/zenodo.8208202

License

CC-BY-4.0

Number of unique molecular configurations

2913

Number of atoms

398991

Elements included

Ge, Sb, Te

Properties included

energy, atomic forces, cauchy stress

Usage

ds.parquet : Aggregated dataset information.
co/ directory: Configuration rows each include a structure, calculated properties, and metadata.
cs/ directory : Configuration sets are subsets of configurations grouped by some common characteristic. If cs/ does not exist, no configurations sets have been defined for this dataset.
cs_co_map/ directory : The mapping of configurations to configuration sets (if defined).

Cite this dataset Zhou, Y., Zhang, W., Ma, E., and Deringer, V. L. GST GAP 22 extended. ColabFit, 2023. https://doi.org/10.60732/37c76fa8