Active-learning accelerated computational screening of A₂B@NG catalysts for CO₂ electrochemical reduction

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dc.contributor.authorLi, X.
dc.contributor.authorLi, H.
dc.contributor.authorZhang, Z.
dc.contributor.authorShi, J.Q.
dc.contributor.authorJiao, Y.
dc.contributor.authorQiao, S.-Z.
dc.date.issued2023
dc.descriptionAvailable online 12 July 2023
dc.description.abstractFew-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.
dc.description.statementofresponsibilityXinyu Li, Haobo Li, Zhen Zhang, Javen Qinfeng Shi, Yan Jiao, Shi-Zhang Qiao
dc.identifier.citationNano Energy, 2023; 115:108695-1-108695-9
dc.identifier.doi10.1016/j.nanoen.2023.108695
dc.identifier.issn2211-2855
dc.identifier.issn2211-2855
dc.identifier.orcidLi, H. [0000-0002-9448-6771]
dc.identifier.orcidZhang, Z. [0000-0003-2805-4396]
dc.identifier.orcidShi, J.Q. [0000-0002-9126-2107]
dc.identifier.orcidJiao, Y. [0000-0003-1329-4290]
dc.identifier.orcidQiao, S.-Z. [0000-0002-1220-1761] [0000-0002-4568-8422]
dc.identifier.urihttps://hdl.handle.net/2440/139973
dc.language.isoen
dc.publisherElsevier
dc.relation.granthttp://purl.org/au-research/grants/arc/DP230102027
dc.relation.granthttp://purl.org/au-research/grants/arc/DP220102596
dc.relation.granthttp://purl.org/au-research/grants/arc/FL170100154
dc.relation.granthttp://purl.org/au-research/grants/arc/FT190100636
dc.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/).
dc.source.urihttps://doi.org/10.1016/j.nanoen.2023.108695
dc.subjectFew-atom catalysts; Machine learning; Electrochemical CO2 reduction; Computational screening
dc.titleActive-learning accelerated computational screening of A₂B@NG catalysts for CO₂ electrochemical reduction
dc.title.alternativeActive-learning accelerated computational screening of A2B@NG catalysts for CO2 electrochemical reduction
dc.typeJournal article
pubs.publication-statusPublished

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