摘要
Anion exchange membrane fuel cells are limited by the slow kinetics of the alkaline hydrogen oxidation reaction (HOR). Aided by density functional theory combined with fine-tuned machine learning interatomic potential, we establish a family of bimetallic catalysts with controlled surface atomic arrangements to identify the optimal catalysts for HOR. Our theoretical analysis successfully predicts the HOR activity rankings of these catalysts, consistent with the experimental results. RuIr exhibits the highest activity, followed by PtRu, AuIr, PtRh, PtIr, PtAu, RhIr, RuRh, AuRu, and AuRh. These trends correlate with the electron-accepting tendencies and the adsorption strengths of H2 and OH* on the catalysts. Among all candidates, RuIr emerges as the most active and durable bimetallic catalyst. Furthermore, operando X-ray absorption spectroscopy and electrochemical measurements reveal a strong synergistic effect of RuIr, where Ir exhibits superior electron-accepting tendency and strong H2 adsorption, while Ru demonstrates strong OH* adsorption, accelerating the alkaline HOR process.