Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations

Abstract Controlling the properties of organic/inorganic materials requires detailed knowledge of their molecular adsorption geometries. This is often unattainable, even with current state-of-the-art tools. Visualizing the structure of complex non-planar adsorbates with atomic force microscopy (AFM) is challenging, and identifying it computationally is intractable with conventional structure search. In a fresh approach, we propose to integrate cross-disciplinary tools for a robust and automated identification of 3D adsorbate configurations. We employ Bayesian optimization with first-principles simulations for accurate and unbiased structure inference of multiple adsorbates. The corresponding AFM simulations then allow us to fingerprint adsorbate structures appearing in AFM experimental images. In the instance of bulky (1S)-camphor adsorbed on the Cu(111) surface, we found three matching AFM image contrasts, which allowed us to correlate experimental image features to distinct cases of molecular adsorption.