Artificial-intelligence-assisted design principle for developing high-performance single-atom catalysts
Artificial intelligence (AI)-assisted approaches are powerful means for advancing catalyst design, as they can significantly accelerate the development of novel catalysts. However, the underlying mechanisms of these approaches often remain elusive, which may lead to unreliable results due to a lack of clear understanding of the involved processes. Herein, we present an AI strategy that combines machine learning (ML) and data mining (DM) to identify high-performance catalysts while elucidating the key factors that govern catalytic performance in complex reactions. Applying this AI strategy to evaluate the electrocatalytic oxygen reduction performance of 10,179 single-atom catalysts (SACs), we identified several high-performance SACs and determined the critical influencers of their activity. Experimental validations further confirm the effectiveness of the AI strategy, with the optimal target Co-S<sub>2</sub>N<sub>2</sub>/g-SAC achieving a high half-wave potential of 0.92 V. This AI-assisted approach significantly enhances the transparency and reliability of data-driven discoveries, providing new insights that benefit the rational design of materials.
Authors
- Xiao Cheng Zeng (ORCID: https://orcid.org/0000-0003-4672-8585)
- Heqing Jiang (ORCID: https://orcid.org/0000-0001-8964-1311)
- Hanxu Yao
- Minghua Huang (ORCID: https://orcid.org/0000-0002-9622-3131)
- Canhui Zhang (ORCID: https://orcid.org/0000-0003-3082-1697)
- Xingkun Wang
- Wenhui Zhao (ORCID: https://orcid.org/0000-0002-2948-5035)
- Dongyoon Woo
- Zhongkang Han (ORCID: https://orcid.org/0000-0003-1489-6824)
- Jinwoo Lee (ORCID: https://orcid.org/0000-0001-6347-0446)
- Liangliang Xu (ORCID: https://orcid.org/0009-0009-9022-4051)
- Xiaofan Li (ORCID: https://orcid.org/0000-0003-4379-3277)
- Ning Xu (ORCID: https://orcid.org/0000-0003-4203-7306)
- Wenwu Xu (ORCID: https://orcid.org/0000-0002-0651-9562)
- Xiaojuan Hu (ORCID: https://orcid.org/0000-0001-7817-2556)
- Yue Wang (ORCID: https://orcid.org/0000-0003-2232-2783)
Institutions
- Ningbo University (CN)
- Korea Advanced Institute of Science and Technology (KR)
- City University of Hong Kong (HK)
- Qingdao Institute of Bioenergy and Bioprocess Technology (CN)
- Hanyang University (KR)
- Ocean University of China (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- The Innovation
- Published
- 2025-04-17
- DOI
- https://doi.org/10.1016/j.xinn.2025.100911
- Citations
- 8
- Primary Topic
- Catalytic Processes in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 4.14
Funders
- National Natural Science Foundation of China