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

A Fantasia and F Rovaris and O Abou El Kheir and A Marzegalli and D Lanzoni and L Pessina and P Xiao and C Zhou and L Li and G Henkelman and E Scalise and F Montalenti, JOURNAL OF CHEMICAL PHYSICS, 161, 014110 (2024).

DOI: 10.1063/5.0214588

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 beta-Sn pressure-induced phase transformation, taken here as an illustrative example.

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