A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations
Tekijät: Järvi Jari, Alldritt Benjamin, Krejci Ondrej, Todorovic Milica, Liljeroth Peter, Rinke Patrick
Kustantaja: WILEY-V C H VERLAG GMBH
Julkaisuvuosi: 2021
Journal: Advanced Functional Materials
Tietokannassa oleva lehden nimi: ADVANCED FUNCTIONAL MATERIALS
Lehden akronyymi: ADV FUNCT MATER
Artikkelin numero: ARTN 2010853
Sivujen määrä: 8
ISSN: 1616-301X
DOI: https://doi.org/10.1002/adfm.202010853
Verkko-osoite: https://doi.org/10.1002/adfm.202010853
Rinnakkaistallenteen osoite: 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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
