Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in germanium.

Autor: Fantasia, A., Rovaris, F., Abou El Kheir, O., Marzegalli, A., Lanzoni, D., Pessina, L., Xiao, P., Zhou, C., Li, L., Henkelman, G., Scalise, E., Montalenti, F.
Předmět:
Zdroj: Journal of Chemical Physics; 7/7/2024, Vol. 161 Issue 1, p1-11, 11p
Abstrakt: 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 β-Sn pressure-induced phase transformation, taken here as an illustrative example. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index