Bayesian inference of atomistic structure in functional materials
Loading...
Access rights
openAccess
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
Date
2019-03-18
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
7
1-7
1-7
Series
npj Computational Materials, Volume 5, issue 1
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 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.Description
| openaire: EC/H2020/676580/EU//NoMaD
Keywords
Other note
Citation
Todorović, M, Gutmann, M U, Corander, J & Rinke, P 2019, ' Bayesian inference of atomistic structure in functional materials ', npj Computational Materials, vol. 5, no. 1, 35, pp. 1-7 . https://doi.org/10.1038/s41524-019-0175-2
