AbstractThe development of oxygen‐dependent fuel cells is frequently hindered by the high cost of cathode materials and the sluggish kinetics of the oxygen reduction reaction (ORR). It is thus crucial to explore low‐cost and high‐activity catalysts with accelerated mass transport. Here, a high‐throughput screening method is developed by integrating an explicit solvation model with machine learning potential‐based molecular dynamics simulations, which enables the efficient evaluation of a vast array of diverse diatomic combinations and ultimately identifying Fe–Co dual‐atomic sites as the optimal ORR electrocatalysts. The superior electrocatalytic performance of the diatomic Fe/Co sites loaded on 3D‐interconnected ordered macroporous carbon (FeCo‐3DMNC) is experimentally verified, achieving high ORR activity with half‐wave potentials of 0.806 V in acidic and 0.905 V in alkaline environments. Additionally, innovative hybrid acid/alkali aluminum–air fuel cells (hA/A‐AAFCs) incorporating FeCo‐3DMNC as the cathode catalyst demonstrate a notably high open‐circuit voltage of 2.72 V and a record‐breaking power density of 827 mW cm−2, significantly outperforming conventional alkaline aluminum–air fuel cells. This work marks a significant advancement by combining cutting‐edge computational screening with rigorous experimental validation to develop promising electrocatalysts, potentially paving the way for the advanced energy storage and conversion technologies.
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