Machine learning driven rational design of AuAgPdHgCu HEA catalysts for the two-electron oxygen reduction reaction
This study integrated high-throughput DFT calculations and machine learning to screen AuAgPdHgCu high-entropy alloy catalysts, revealing that negative d-band shifts of Hg/Cu optimize Δ<i>G</i><sub>*OOH</sub> for an enhanced 2e<sup>-</sup> ORR activity. Structure-activity analysis identified an optimal configuration (0.97 ideal active sites), guiding efficient catalyst design.
Authors
- Shuqin Song (ORCID: https://orcid.org/0000-0002-7459-6738)
- Bihua Hu (ORCID: https://orcid.org/0000-0002-2796-1333)
- Xin Wang (ORCID: https://orcid.org/0000-0003-2686-466X)
- Xi Liu (ORCID: https://orcid.org/0000-0002-3152-5390)
- Zhongwei Chen (ORCID: https://orcid.org/0000-0003-3463-5509)
- Lin Yang
- Junyi Zhu (ORCID: https://orcid.org/0009-0005-3502-7865)
- Zhen Chen (ORCID: https://orcid.org/0009-0007-7842-5446)
Institutions
- Zhejiang Wanli University (CN)
- Sun Yat-sen University (CN)
- Dalian Institute of Chemical Physics (CN)
Publication Details
- Journal
- Chemical Communications
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5cc03076e
- Citations
- 1
- Primary Topic
- Electrocatalysts for Energy Conversion
- Type
- article
- Field-Weighted Citation Impact
- 0.36
Funders
- Zhejiang Wanli University
- National Natural Science Foundation of China
- Department of Education of Zhejiang Province