Complex Ga_(2)O_(3) polymorphs explored by accurate and general-purpose machine-learning interatomic potentials  

作　　者：Junlei Zhao Jesper Byggmästar Huan He Kai Nordlund Flyura Djurabekova Mengyuan Hua 

机构地区：[1]Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen 518055,China [2]Department of Physics,University of Helsinki,P.O.Box 43,FI-00014 Helsinki,Finland [3]FCAI:Finnish Center for Artificial Intelligence,University of Helsinki,P.O.Box 43,FI-00014 Helsinki,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,P.O.Box 43,FI-00014 Helsinki,Finland

出　　处：《npj Computational Materials》2023年第1期666-675,共10页计算材料学（英文）

基　　金：This work is supported by Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515012048;Shenzhen Fundamental Research Program under Grant JCYJ20220530114615035;This work is also supported in part by the National Natural Science Foundation under Grant 62104092;Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515011952;F.D.acknowledges M-ERA.NET Program for financial support via GOFIB project administrated in Finland by the Academy of Finland project number 352518;F.D.,K.N.and J.B.acknowledge the international collaboration within the COST Action FIT4NANO CA19140 supported by the European Cooperation in Science and Technology,https://www.cost.eu/.The computational resource is supported by the Center for Computational Science and Engineering at the Southern University of Science and Technology.The authors are also grateful to the grants of computer power from CSC-IT Center for Science,Finland.J.Z.also acknowledge Prof.L.-J.Zhang and Prof.Y.-H.Fu at Jilin University,for the helpful discussion on the CALYPSO-predicted P1 and Pmc21Ga2O_(3) structures50.The authors are also grateful to Dr.H.Liu and Dr.I.Makkonen at the University of Helsinki and Prof.A.Kuznetsov at the University of Oslo for the insightful discussion.

摘　　要：Ga_(2)O_(3) is a wide-band gap semiconductor of emergent importance for applications in electronics and optoelectronics.However,vital information of the properties of complex coexisting Ga_(2)O_(3) polymorphs and low-symmetry disordered structures is missing.We develop two types of machine-learning Gaussian approximation potentials(ML-GAPs)for Ga_(2)O_(3) with high accuracy forβ/κ/α/δ/γpolymorphs 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 5×102 and 2×105 computing speed increases compared to density functional theory,respectively.Moreover,the Ga_(2)O_(3) 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.

关 键 词：DISORDERED exceptional EXCEEDING 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]