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.