Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)

Author(s): Peitao Liu, Jiantao Wang, Noah Avargues, Carla Verdi, Andreas Singraber, Ferenc Karsai, Xing-Qiu Chen, Georg Kresse
Abstract: Adsorption of carbon monoxide (CO) on transition-metal surfaces is a prototypical process in surface sciences and catalysis. Despite its simplicity, it has posed great challenges to theoretical modeling. Pretty much all existing density functionals fail to accurately describe surface energies and CO adsorption site preference as well as adsorption energies simultaneously. Although the random phase approximation (RPA) cures these density functional theory failures, its large computational cost makes it prohibitive to study the CO adsorption for any but the simplest ordered cases. Here, we address these challenges by developing a machine-learned force field (MLFF) with near RPA accuracy for the prediction of coverage-dependent adsorption of CO on the Rh(111) surface through an efficient on-the-fly active learning procedure and a Δ-machine learning approach. We show that the RPA-derived MLFF is capable to accurately predict the Rh(111) surface energy and CO adsorption site preference as well as adsorption energies at different coverages that are all in good agreement with experiments. Moreover, the coverage-dependent ground-state adsorption patterns and adsorption saturation coverage are identified.
Organisation(s): Computational Materials Physics
External organisation(s): Chinese Academy of Sciences (CAS), VASP Software GmbH
Journal: Physical Review Letters
Volume: 130
No. of pages: 6
ISSN: 0031-9007
DOI: https://doi.org/10.1103/PhysRevLett.130.078001
Publication date: 02-2023
Peer reviewed: Yes
Austrian Fields of Science 2012: 103015 Condensed matter, 103043 Computational physics, 102019 Machine learning
ASJC Scopus subject areas: General Physics and Astronomy
Portal url: https://ucrisportal.univie.ac.at/en/publications/f1ba1345-06e4-4ba7-b5ad-f5b06cde3a4b