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

TL;DR

This paper presents a novel integrated approach combining Bayesian inference, first-principles simulations, and AFM imaging to accurately identify complex 3D surface adsorbate structures, overcoming limitations of traditional methods.

Contribution

It introduces a cross-disciplinary method that automates and enhances the identification of molecular adsorption geometries using Bayesian optimization and AFM simulations.

Findings

Successfully identified three distinct AFM image contrasts for (1S)-camphor on Cu(111)

Demonstrated the effectiveness of Bayesian inference in structure determination

Enabled correlation between AFM images and molecular adsorption 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 this fresh approach, cross-disciplinary tools are integrated for a robust and automated identification of 3D adsorbate configurations. Bayesian optimization is employed with first-principles simulations for accurate and unbiased structure inference of multiple adsorbates. The corresponding AFM simulations then allow fingerprinting adsorbate structures that appear in AFM experimental images. In the instance of bulky (1S)-camphor adsorbed on the Cu(111) surface, three matching AFM image contrasts are found, which allow correlating experimental image features to distinct cases of molecular adsorption.