Projects per year
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.
Original language | English |
---|---|
Pages (from-to) | 1577-1589 |
Number of pages | 13 |
Journal | Beilstein Journal of Nanotechnology |
Volume | 11 |
DOIs | |
Publication status | Published - 19 Oct 2020 |
MoE publication type | A1 Journal article-refereed |
Keywords
- 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
Fingerprint
Dive into the research topics of 'Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization'. Together they form a unique fingerprint.Datasets
-
(1S)-camphor on Cu(111), 6D surface adsorbate search with BOSS
Järvi, J. (Creator), Todorovic, M. (Creator), Rinke, P. (Creator), Alldritt, B. (Contributor), Krejci, O. (Contributor) & Liljeroth, P. (Other), Zenodo, 2021
DOI: 10.5281/zenodo.4680466, https://doi.org/10.5281/zenodo.4680467
Dataset
Projects
- 1 Finished
-
Artificial Intelligence for Microscopic Structure Search
Rinke, P., Lehto, E., Geurts, A., Ghosh, K., Himanen, L., Li, J., Todorovic, M. & Homm, H.
01/01/2018 → 31/12/2021
Project: Academy of Finland: Other research funding