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.

Fantasia, A., Rovaris, F., Abou El Kheir, O., Marzegalli, A., Lanzoni, D., Pessina, L., et al. (2024). Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in germanium. THE JOURNAL OF CHEMICAL PHYSICS, 161(1), 1-11 [10.1063/5.0214588].

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

Fantasia A.
;
Rovaris F.;Abou El Kheir O.;Marzegalli A.;Lanzoni D.;Scalise E.;Montalenti F.
2024

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 β-Sn pressure-induced phase transformation, taken here as an illustrative example.
Articolo in rivista - Articolo scientifico
Germanium, Machine Learning Interatomic Potentials (MLIPs), Density Functional Theory (DFT), Solid-state nudged elastic band (ssNEB), Phase transitions
English
2-lug-2024
2024
161
1
1
11
014110
open
Fantasia, A., Rovaris, F., Abou El Kheir, O., Marzegalli, A., Lanzoni, D., Pessina, L., et al. (2024). Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in germanium. THE JOURNAL OF CHEMICAL PHYSICS, 161(1), 1-11 [10.1063/5.0214588].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/516339
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