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 comple...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).展开更多
基金funded in part by the Austrian Science Fund(FWF)10.55776/F81。
文摘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).
