Bayesian inference of atomistic structure in functional materials

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Todorovic , M , Gutmann , M U , Corander , J & Rinke , P 2019 , ' Bayesian inference of atomistic structure in functional materials ' , Npj computational materials , vol. 5 , 35 .

Title: Bayesian inference of atomistic structure in functional materials
Author: Todorovic, Milica; Gutmann, Michael U.; Corander, Jukka; Rinke, Patrick
Other contributor: University of Helsinki, University of Edinburgh
University of Helsinki, Department of Mathematics and Statistics

Date: 2019-03-18
Language: eng
Number of pages: 7
Belongs to series: Npj computational materials
ISSN: 2057-3960
Abstract: 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 TiO2 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.
111 Mathematics

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