Polaron defects are ubiquitous in materials and play an important role in many processes involving carrier mobility,charge transfer and surface reactivity.Determining small polarons’spatial distributions is essential...Polaron defects are ubiquitous in materials and play an important role in many processes involving carrier mobility,charge transfer and surface reactivity.Determining small polarons’spatial distributions is essential to understand materials properties and functionalities.However,the required exploration of the configurational space is computationally demanding when using first principles methods.Here,we propose a machine-learning(ML)accelerated search that determines the ground state polaronic configuration.The ML model is trained on databases of polaron configurations generated by density functional theory(DFT)via molecular dynamics or random sampling.To establish a mapping between configurations and their stability,we designed descriptors modelling the interactions among polarons and charged point defects.We used the DFT+ML protocol to explore the polaron configurational space for two surface-systems,reduced rutile TiO_(2)(110)and Nb-doped SrTiO_(3)(001).The ML-aided search proposes additional polaronic configurations and can be utilized to determine optimal polaron distributions at any charge concentration.展开更多
基金This work was supported by the Austrian Science Fund(FWF)project POLOX(Grant No.I 2460-N36),project Super(Grant No.P 32148-N36),the SFB-F81 project TACO.
文摘Polaron defects are ubiquitous in materials and play an important role in many processes involving carrier mobility,charge transfer and surface reactivity.Determining small polarons’spatial distributions is essential to understand materials properties and functionalities.However,the required exploration of the configurational space is computationally demanding when using first principles methods.Here,we propose a machine-learning(ML)accelerated search that determines the ground state polaronic configuration.The ML model is trained on databases of polaron configurations generated by density functional theory(DFT)via molecular dynamics or random sampling.To establish a mapping between configurations and their stability,we designed descriptors modelling the interactions among polarons and charged point defects.We used the DFT+ML protocol to explore the polaron configurational space for two surface-systems,reduced rutile TiO_(2)(110)and Nb-doped SrTiO_(3)(001).The ML-aided search proposes additional polaronic configurations and can be utilized to determine optimal polaron distributions at any charge concentration.
