Bayesian inference of atomistic structure in functional materials  

作　　者：Milica Todorović Michael U.Gutmann Jukka Corander Patrick Rinke 

机构地区：[1]Department of Applied Physics,Aalto University,P.O.Box 11100,Aalto FI-00076,Finland [2]School of Informatics,University of Edinburgh,10 Crichton Street,Edinburgh EH89AB,UK [3]Institute of Basic Medical Sciences,University of Oslo,Sognsvannsveien 9,0372 Oslo,Norway [4]Department of Mathematics and Statistics,University of Helsinki,P.O.Box 68,Helsinki FI-00014,Finland

出　　处：《npj Computational Materials》2019年第1期836-842,共7页计算材料学（英文）

基　　金：This work was supported by the Academy of Finland through Project Nos.251748,284621 and 316601,and also through the European Union’s Horizon 2020 research and innovation programme under Grant agreement No.676580;The Novel Materials Discovery(NOMAD)Laboratory,a European Center of Excellence.J.C.was funded by the ERC grant no.742158.

摘　　要：Tailoring the functional properties of advanced organic/inorganic heterogeneous devices to their intended technological applications requires knowledge and control of the microscopic structure inside the device.Atomistic quantum mechanical simulation methods deliver accurate energies and properties for individual configurations,however,finding the most favourable configurations remains computationally prohibitive.We propose a‘building block’-based Bayesian Optimisation Structure Search(BOSS)approach for addressing extended organic/inorganic interface problems and demonstrate its feasibility in a molecular surface adsorption study.In BOSS,a Bayesian model identifies material energy landscapes in an accelerated fashion from atomistic configurations sampled during active learning.This allowed us to identify several most favourable molecular adsorption configurations for C_(60) on the(101)surface of TiO_(2) anatase and clarify the key molecule-surface interactions governing structural assembly.Inferred structures were in good agreement with detailed experimental images of this surface adsorbate,demonstrating good predictive power of BOSS and opening the route towards large-scale surface adsorption studies of molecular aggregates and films.

关 键 词：ADSORPTION FUNCTIONAL INORGANIC 

分 类 号：O64[理学—物理化学]