A1 Refereed original research article in a scientific journal
Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations
Authors: Järvi Jari, Alldritt Benjamin, Krejci Ondrej, Todorovic Milica, Liljeroth Peter, Rinke Patrick
Publisher: WILEY-V C H VERLAG GMBH
Publication year: 2021
Journal: Advanced Functional Materials
Journal name in source: ADVANCED FUNCTIONAL MATERIALS
Journal acronym: ADV FUNCT MATER
Article number: ARTN 2010853
Number of pages: 8
ISSN: 1616-301X
DOI: https://doi.org/10.1002/adfm.202010853
Web address : https://doi.org/10.1002/adfm.202010853
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/58230693
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.
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