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Wu et al. J. Mater. Inf. 2025, 5, 14 Journal of
DOI: 10.20517/jmi.2024.77
Materials Informatics

Research Article Open Access

Machine-learning prediction of facet-dependent CO
coverage on Cu electrocatalysts

1,*
1
1
2,*
1
Shanglin Wu , Shisheng Zheng , Wentao Zhang , Mingzheng Zhang , Shunning Li , Feng Pan 1,*
1
School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, Guangdong, China.
2
College of Energy, Xiamen University, Xiamen 361000, Fujian, China.
*
Correspondence to: Prof. Shunning Li, Prof. Feng Pan, School of Advanced Materials, Peking University, Shenzhen Graduate
School, No. 2199 Lishui Road, Shenzhen 518055, Guangdong, China. E-mail: lisn@pku.edu.cn, panfeng@pkusz.edu.cn; Prof.
Shisheng Zheng, College of Energy, Xiamen University, No. 4221, Xiang’an South Road, Xiamen 361000, Fujian, China. E-mail:
zhengss@pku.edu.cn
How to cite this article: Wu, S.; Zheng, S.; Zhang, W.; Zhang, M.; Li, S.; Pan, F. Machine-learning prediction of facet-dependent
CO coverage on Cu electrocatalysts. J. Mater. Inf. 2025, 5, 14. https://dx.doi.org/10.20517/jmi.2024.77
Received: 22 Nov 2024 First Decision: 19 Dec 2024 Revised: 16 Jan 2025 Accepted: 20 Jan 2025 Published: 26 Feb 2025
Academic Editors: Lei Shen, Ming Hu Copy Editor: Ting-Ting Hu Production Editor: Ting-Ting Hu

Abstract
Copper-based electrocatalysts, which hold great promise in selectively reducing CO into multicarbon products,
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have attracted significant recent interest, both experimentally and theoretically. While many studies have
suggested a strong dependence of catalytic selectivity on the concentration of the CO reaction intermediate on the
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Cu surface, it remains challenging for a direct experimental probe of the CO coverage. This necessitates a reliable
computational method that can accurately establish the theoretical coverage-dependent phase diagram of CO
adsorbates on the catalyst. Here we propose a scheme composed of density functional theory calculations,
machine-learning force fields and graph neural networks as a solution. This method enables a fast screening of
7 million adsorption configurations based on a small set of density functional theory data, with a balance between
accuracy and efficiency tuned by the combinatorial use of machine-learning force field and graph neural network
models. We have investigated eight different Cu facets and discovered that the high-index facets such as (310),
(210) and (322) exhibit a much higher CO coverage than the low-index counterparts such as (111), leading to an
increased opportunity for C–C coupling for the former. Our results can provide a new perspective for the
understanding of the fundamental role of CO coverage on the Cu surface for electrochemical CO reduction.
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Keywords: Machine-learning force fields, density functional theory, graph neural networks, coverage effect,
electrochemical CO reduction
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© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
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