Smart Sampling for Chemical Property Landscapes with BOSS

A Seminar of the NOMAD Laboratory

  • Online Seminar of the NOMAD Laboratory
  • Date: Jan 14, 2021
  • Time: 13:15
  • Speaker: Milica Todorović
  • Aalto University, Finland
  • Location:
  • Room: Webinar ID: 835 7526 9594 | Password: NOMAD
  • Host: NOMAD Laboratory
Smart Sampling for Chemical Property Landscapes with BOSS
Atomistic structure search for organic/inorganic heterostructures is made complex by the many degrees of freedom and the need for accurate but costly density-functional theory (DFT) simulations. To accelerate and simplify structure determination in such heterogeneous functional materials, we developed the Bayesian Optimization Structure Search (BOSS) approach [1]. BOSS builds N-dimensional surrogate models for the energy or property landscapes to infer global optima. The models are iteratively refined by sequentially sampling DFT data points with high information content. The uncertainty-led exploration/exploitation sampling strategy delivers global minima with modest sampling, but also ensures visits to less favorable regions of phase space to gather information on rare events and energy barriers.

We applied this active learning scheme to study 1) molecular adsorption at the organic/inorganic interfaces and 2) molecular conformers. For 1), we studied camphor deposited on Cu(111) and identified 8 unique stable adsorbates [2]. By comparing them to atomic force microscopy (AFM) images, we were able to recognize 3 different structures of chemisorbed camphor in experiments [3]. For 2), we applied BOSS to conformer search of several amino acid molecules. We successfully recovered more than 10 experimentally and theoretically determined conformer structures in less than 10% computational cost of the current fastest method [4]. With a recent batch implementation for active learning, BOSS can make use of exascale computing resources to solve large-scale structural problems without sacrificing quantum-mechanical accuracy.


[1] M. Todorović, M. U. Gutmann, J. Corander and P. Rinke, ‘Efficient Bayesian Inference of Atomistic Structure in Complex Functional Materials’, npj Comput. Mater., 5, 35 (2019)

[2] J. Järvi, P. Rinke and M. Todorović, ‘Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization’ Beilstein J. Nanotechnol. 11, 1577-1589 (2020)

[3] J. Järvi, B. Aldritt, O. Krejčí, M. Todorović, P. Liljeroth and P. Rinke, ‘Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations’, under consideration at Adv. Funct. Mater., doi:10.21203/, (2020)

[4] L. Fang, E. Makkonen, M. M. Todorović, P. Rinke, X. Chen, ’Efficient Cysteine Conformer Search with Bayesian Optimization’, accepted at JCTC, arXiv:2006.15006 (2020).

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