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Title: Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in germanium
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.  more » « less
Award ID(s):
2102317
PAR ID:
10566100
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ;
Publisher / Repository:
American Physical Society
Date Published:
Journal Name:
The Journal of Chemical Physics
Volume:
161
Issue:
1
ISSN:
0021-9606
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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