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

Beskrivning

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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Publiceringsår

2025

Typ av data

Upphovspersoner

Ali Hamedani - Medarbetare

Andrea E. Sand - Medarbetare

Ruoyan Jin - Upphovsperson, Utgivare

Projekt

Övriga uppgifter

Vetenskapsområden

Fysik

Språk

Öppen tillgång

Öppet

Licens

Creative Commons Attribution 4.0 International (CC BY 4.0)

Nyckelord

radiation damage, germanium, Gaussian Approximation Potential

Ämnesord

Temporal täckning

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