Machine learning-based prediction of polaron-vacancy patterns on the TiO_(2)(110)surface  

作　　者：Viktor C.Birschitzky Igor Sokolović Michael Prezzi Krisztián Palotás Martin Setvín Ulrike Diebold Michele Reticcioli Cesare Franchini 

机构地区：[1]Faculty of Physics and Center for Computational Materials Science,University of Vienna,Vienna,Austria [2]Institute of Applied Physics,TU Wien,Vienna,Austria [3]Institute for Solid State Physics and Optics,HUN-REN Wigner Research Center for Physics,Budapest,Hungary [4]Department of Surface and Plasma Science,Faculty of Mathematics and Physics,Charles University,Prague,Czech Republic [5]Dipartimento di Fisica e Astronomia,Universitàdi Bologna,Bologna,Italy

出　　处：《npj Computational Materials》2024年第1期2328-2336,共9页计算材料学(英文)

基　　金：funded in part by the Austrian Science Fund(FWF)10.55776/F81。

摘　　要：The multifaceted physics of oxides is shaped by their composition and the presence of defects,which are often accompanied by the formation of polarons.The simultaneous presence of polarons and defects,and their complex interactions,pose challenges for first-principles simulations and experimental techniques.In this study,weleveragemachine learning and a first-principles database to analyze the distribution of surface oxygen vacancies(VO)and induced small polarons on rutile TiO_(2)(110),effectively disentangling the interactions between polarons and defects.By combining neural-network supervised learning and simulated annealing,we elucidate the inhomogeneous VO distribution observed in scanning probe microscopy(SPM).

关 键 词：POLARON SURFACE distribution 

分 类 号：TG1[金属学及工艺—金属学]