Active-learning accelerated computational screening of A₂B@NG catalysts for CO₂ electrochemical reduction
Date
2023
Authors
Li, X.
Li, H.
Zhang, Z.
Shi, J.Q.
Jiao, Y.
Qiao, S.-Z.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Nano Energy, 2023; 115:108695-1-108695-9
Statement of Responsibility
Xinyu Li, Haobo Li, Zhen Zhang, Javen Qinfeng Shi, Yan Jiao, Shi-Zhang Qiao
Conference Name
Abstract
Few-atom catalysts, due to the unique coordination structure compared to metal particles and single-atom catalysts, have the potential to be applied for efficient electrochemical CO2 reduction (CRR). In this study, we designed a class of triple-atom A2B catalysts, with two A metal atoms and one B metal atom either horizontally or vertically embedded in the nitrogen-doped graphene plane. Metals A and B were selected from 17 elements across 3d to 5d transition metals. The structural stability and CRR activity of the 257 constructed A2B catalysts were evaluated. The active-learning approach was applied to predict the adsorption site of key reaction intermediate *CO, which only used 40% computing resources in comparison to “brute force” calculation and greatly accelerated the large amount of computation brought by the large number of A2B catalysts. Our results reveal that these triple atom catalysts can selectively produce more valuable hydrocarbon products while preserving high reactivity. Additionally, six triple-atom catalysts were proposed as potential CRR catalysts. These findings provide a theoretical understanding of the experimentally synthesized Fe3 and Ru3-N4 catalysts and lay a foundation for future discovery of few-atom catalysts and carbon materials in other applications. A new machine learning method, masked energy model, was also proposed which outperforms existing methods by approximately 5% when predicting low-coverage adsorption sites.
School/Discipline
Dissertation Note
Provenance
Description
Available online 12 July 2023
Access Status
Rights
© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).