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

Fantasia, A.1; Rovaris, F.1; Abou El Kheir, O.1; Marzegalli, A.1; Lanzoni, D.1; Pessina, L.1; Xiao, P.2; Zhou, C.3; Li, L.3; Henkelman, G.4; Scalise, E.1; Montalenti, F.1

2024-07-07

10.1063/5.0214588

JOURNAL OF CHEMICAL PHYSICS   影响因子和分区

0021-9606

1089-7690

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.

SCI ; EI

英语

其他

National Science Foundation[CHE-2102317] ; European Union-NextGenerationEU-Project Title "SiGe Hexagonal Diamond Phase by nanoIndenTation (HD- PIT)"[CUP H53D23000780001]

Chemistry ; Physics

Chemistry, Physical ; Physics, Atomic, Molecular & Chemical

WOS:001262307000006

AIP Publishing

20242816671298

Density functional theory ; Design for testability ; Machine learning

Nonferrous Metals and Alloys excluding Alkali and Alkaline Earth Metals:549.3 ; Artificial Intelligence:723.4 ; Probability Theory:922.1 ; Atomic and Molecular Physics:931.3 ; Quantum Theory; Quantum Mechanics:931.4

CHEMISTRY

Web of Science

1.Univ Milano Bicocca, Dept Mat Sci, I-20125 Milan, Italy
2.Dalhousie Univ, Dept Phys & Atmospher Sci, 1453 Lord Dalhousie Dr, Halifax, NS B3H 4R2, Canada
3.Southern Univ Sci & Technol, Dept Mat Sci & Engn, 1088 Xueyuan Ave, Shenzhen 518055, Peoples R China
4.Univ Texas Austin, Dept Chem, 105 East 24th St STOP A5300, Austin, TX 78712 USA

Fantasia, A.,Rovaris, F.,Abou El Kheir, O.,et al. Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in germanium[J]. JOURNAL OF CHEMICAL PHYSICS,2024,161(1).

Fantasia, A..,Rovaris, F..,Abou El Kheir, O..,Marzegalli, A..,Lanzoni, D..,...&Montalenti, F..(2024).Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in germanium.JOURNAL OF CHEMICAL PHYSICS,161(1).

Fantasia, A.,et al."Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in germanium".JOURNAL OF CHEMICAL PHYSICS 161.1(2024).