Machine learning driven rational design of AuAgPdHgCu HEA catalysts for two-electron oxygen reduction reaction

Abstract

This study integrated high-throughput DFT calcuulations and machine learning to screen AuAgPdHgCu high-entropy alloy catalysts, revealing that negative d-band shifts of Hg/Cu optimize ΔG*OOH for enhanced 2e⁻ ORR activity. Structural-activity analysis identified an optimal configuration (0.97 ideal active sites), guiding efficient catalyst design.

Supplementary files

Article information

Article type
Communication
Submitted
30 May 2025
Accepted
11 Jul 2025
First published
25 Jul 2025

Chem. Commun., 2025, Accepted Manuscript

Machine learning driven rational design of AuAgPdHgCu HEA catalysts for two-electron oxygen reduction reaction

Z. Chen, X. Liu, J. Zhu, B. Hu, L. Yang, X. Wang, S. Song and Z. Chen, Chem. Commun., 2025, Accepted Manuscript , DOI: 10.1039/D5CC03076E

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