Bayesian inference of atomistic structure in functional materials




Todorovic M, Gutmann MU, Corander J, Rinke P

PublisherNATURE PUBLISHING GROUP

2019

npj Computational Materials

NPJ COMPUTATIONAL MATERIALS

NPJ COMPUT MATER

35

5

7

DOIhttps://doi.org/10.1038/s41524-019-0175-2



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.



Last updated on 2024-26-11 at 11:59