Machine learning driven rational design of AuAgPdHgCu HEA catalysts for 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 ΔG*OOH for an enhanced 2e- ORR activity. Structure-activity analysis identified an optimal configuration (0.97 ideal active sites), guiding efficient catalyst design.