Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in germanium.
J Chem Phys
; 161(1)2024 Jul 07.
Article
em En
| MEDLINE
| ID: mdl-38953439
ABSTRACT
We introduce a data-driven potential aimed at the investigation of pressure-dependent phase transitions in bulk germanium, including the estimate of kinetic barriers. This is achieved by suitably building a database including several configurations along minimum energy paths, as computed using the solid-state nudged elastic band method. After training the model based on density functional theory (DFT)-computed energies, forces, and stresses, we provide validation and rigorously test the potential on unexplored paths. The resulting agreement with the DFT calculations is remarkable in a wide range of pressures. The potential is exploited in large-scale isothermal-isobaric simulations, displaying local nucleation in the R8 to ß-Sn pressure-induced phase transformation, taken here as an illustrative example.
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01-internacional
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MEDLINE
Idioma:
En
Revista:
J Chem Phys
Ano de publicação:
2024
Tipo de documento:
Article