Bayesian inference of atomistic structure in functional materials

Research output: Contribution to journalArticleScientificpeer-review


Research units

  • Institute for Adaptive and Neural Computation
  • University of Edinburgh
  • University of Oslo
  • University of Helsinki


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.


Original languageEnglish
Article number35
Pages (from-to)1-7
Number of pages7
Journalnpj Computational Materials
Issue number1
Publication statusPublished - 18 Mar 2019
MoE publication typeA1 Journal article-refereed

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