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 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.

arXiv comment9 pages, 4 figures, 1 table

References in corpus (6)

• The mechanism of high-resolution STM/AFM imaging with functionalized tips
• The origin of high-resolution IETS-STM images of organic molecules with functionalized tips
• Efficient Bayesian Inference of Atomistic Structure in Complex Functional Materials
• Automated Structure Discovery in Atomic Force Microscopy
• Submolecular resolution by variation of IETS amplitude and its relation to AFM/STM signal
• Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization

Cited by in corpus (2)

• Molecular Identification from AFM images using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks
• Protective Coating Interfaces for perovskite Solar Cell Materials: A first Principles Study