Complex Ga2O3 polymorphs explored by accurate and general-purpose machine-learning interatomic potentials

Zhao，Junlei1; Byggmästar，Jesper2,3; He，Huan2,4; Nordlund，Kai2,5; Djurabekova，Flyura2,5; Hua，Mengyuan1

2023-12-01

10.1038/s41524-023-01117-1

npj Computational Materials   Impact Factor & Quartile

2057-3960

GaO is a wide-band gap semiconductor of emergent importance for applications in electronics and optoelectronics. However, vital information of the properties of complex coexisting GaO polymorphs and low-symmetry disordered structures is missing. We develop two types of machine-learning Gaussian approximation potentials (ML-GAPs) for GaO with high accuracy for β/κ/α/δ/γ polymorphs and generality for disordered stoichiometric structures. We release two versions of interatomic potentials in parallel, namely soapGAP and tabGAP, for high accuracy and exceeding speedup, respectively. Both potentials can reproduce the structural properties of all the five polymorphs in an exceptional agreement with ab initio results, meanwhile boost the computational efficiency with 5 × 10 and 2 × 10 computing speed increases compared to density functional theory, respectively. Moreover, the GaO liquid-solid phase transition proceeds in three different stages. This experimentally unrevealed complex dynamics can be understood in terms of distinctly different mobilities of O and Ga sublattices in the interfacial layer.

English

First ; Corresponding

2-s2.0-85169666792

Scopus

1.Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Department of Physics,University of Helsinki,Helsinki,P.O. Box 43,FI-00014,Finland
3.FCAI: Finnish Center for Artificial Intelligence,University of Helsinki,Helsinki,P.O. Box 43,FI-00014,Finland
4.School of Nuclear Science and Technology,Xi’an Jiaotong University,Xi’an,Shaanxi,710049,China
5.Helsinki Institute of Physics,University of Helsinki,Helsinki,P.O. Box 43,FI-00014,Finland

Zhao，Junlei,Byggmästar，Jesper,He，Huan,et al. Complex Ga2O3 polymorphs explored by accurate and general-purpose machine-learning interatomic potentials[J]. npj Computational Materials,2023,9(1).

Zhao，Junlei,Byggmästar，Jesper,He，Huan,Nordlund，Kai,Djurabekova，Flyura,&Hua，Mengyuan.(2023).Complex Ga2O3 polymorphs explored by accurate and general-purpose machine-learning interatomic potentials.npj Computational Materials,9(1).

Zhao，Junlei,et al."Complex Ga2O3 polymorphs explored by accurate and general-purpose machine-learning interatomic potentials".npj Computational Materials 9.1(2023).