Abstract
High-entropy alloys (HEAs) are increasingly recognised as promising catalyst materials due to their highly tunable compositions. In particular, the Cantor alloy, CrCoFeMnNi, has attracted significant interest as a low-cost, earth-abundant alternative to Pt-group catalysts. However, the Cantor alloy is susceptible to degradation in acidic environments. To address this, Pt has been proposed as an addition to the Cantor alloy to leverage the high catalytic activity and surface stability of Pt. In this study, we identify and elucidate the catalytic activity trend for the oxygen reduction reaction (ORR) across the CrCoFeMnNiPt composition space. Assuming the formation of a Pt skin through surface reconfiguration, a machine learning-accelerated simulation protocol infers the strain-corrected adsorption strengths of key reaction intermediates and uses them as descriptors for kinetic modelling. Our results show that Pt-modification of the Cantor alloy is a potentially viable strategy towards active and durable ORR catalysts. However, we also observe binary Pt-rich alloys with approximately 80% Pt exhibit the highest ORR activity due to synergistic ligand and strain effects. As this analysis identifies known alloys with high ORR performance, we suggest that this framework contributes to a clear understanding of how bulk composition affects the reaction kinetics of complex surface alloys.
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