Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date

2020-10-19

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Department of Applied Physics

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en

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13

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Beilstein Journal of Nanotechnology, Volume 11, pp. 1577-1589

Abstract

Identifying the atomic structure of organic-inorganic interfaces is challenging with current research tools. Interpreting the structure of complex molecular adsorbates from microscopy images can be difficult, and using atomistic simulations to find the most stable structures is limited to partial exploration of the potential energy surface due to the high-dimensional phase space. In this study, we present the recently developed Bayesian Optimization Structure Search ( BOSS) method as an efficient solution for identifying the structure of non-planar adsorbates. We apply BOSS with density-functional theory simulations to detect the stable adsorbate structures of (1S)-camphor on the Cu(111) surface. We identify the optimal structure among eight unique types of stable adsorbates, in which camphor chemisorbs via oxygen (global minimum) or physisorbs via hydrocarbons to the Cu(111) surface. This study demonstrates that new cross-disciplinary tools, such as BOSS, facilitate the description of complex surface structures and their properties, and ultimately allow us to tune the functionality of advanced materials.

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Bayesian optimization, camphor, Cu(111), density-functional theory, electronic structure, organic surface adsorbates, physical chemistry, structure search, surface science, ATOMIC-FORCE MICROSCOPY, SURFACE, MOLECULES, SEARCH

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DOI

10.3762/bjnano.11.140

Citation

Jarvi, J, Rinke, P & Todorovic, M 2020, 'Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization', Beilstein Journal of Nanotechnology, vol. 11, pp. 1577-1589. https://doi.org/10.3762/bjnano.11.140

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https://urn.fi/URN:NBN:fi:aalto-2020113020630

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[article-cris] Perustieteiden korkeakoulu / SCI