Identifying Surface Adsorbate Structures with Bayesian Inference and Atomic Force Microscopy

Determining stable structures of organic molecular adsorbates requires both quantum mechanics and thorough exploration of the potential energy surface (PES). This is prohibitively expensive with density-functional theory (DFT). Bayesian Optimization Structure Search (BOSS) [1] is a new tool that combines DFT with Bayesian inference for accurate global structure search. BOSS applies strategic sampling to compute the complete PES with a small number of expensive DFT simulations. This allows a clear identification of stable structures and their energy barriers.

We apply BOSS to study the adsorption of (1S)-camphor on the Cu(111) surface as a function of molecular orientation and translations [2]. We identify 8 unique adsorbate types, in which camphor chemisorbs (global minimum) or physisorbs to the Cu(111) surface. We employ the most stable structures to produce simulated atomic force microscopy (AFM) images, which we use to identify adsorbate configurations in AFM experiments [3]. This study demonstrates the power of cross-disciplinary tools in detecting complex interface structures.

[1] M. Todorović et al., npj Comput. Mater. 2019, 5, 35.
[2] J. Järvi et al., Beilstein J. Nanotechnol. 2020, 11, 1577-1589.
[3] J. Järvi et al., in preparation. doi:10.21203/rs.3.rs-50783/v1.