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.
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.
