Machine learning interatomic potential for studying radiation effects in germanium - The dataset

Description

This dataset supplies training data for a Gaussian Approximation Potential for germanium, developed specifically for radiation damage studies. It encompasses 451 structures from dimers, multiple bulk crystal phases, liquid configurations at various temperatures, a diverse range of defect structures, and other relevant configurations. All structures are stored in extended XYZ format, with each configuration annotated by total energy, atomic forces, and virial stresses calculated via DFT at the PBE level using VASP. Additional details on dataset generation and DFT calculations are provided in the published paper.
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Year of publication

2025

Type of data

Authors

Ali Hamedani - Contributor

Andrea E. Sand - Contributor

Ruoyan Jin - Creator, Publisher

Project

Other information

Fields of science

Physical sciences

Language

Open access

Open

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Keywords

radiation damage, germanium, Gaussian Approximation Potential

Subject headings

Temporal coverage

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