Computational Screening of Pt-Skin Oxygen Reduction Reaction Electrocatalyst Supported by Random Solid-Solution PtPdRhIrRu Alloy
High-entropy alloy (HEA) catalysts provide a vast compositional design space for optimizing the oxygen reduction reaction (ORR), but their complexity presents significant challenges for computational screening. In this work, we develop a machine-learning-assisted framework to screen Pt-skin ORR electrocatalysts supported on IrPdPtRhRu HEA subsurface using *O and *OH binding energies as descriptors of catalytic activity. The model employs a simple multiple linear regression approach combined with zone-based representations of the local atomic environment to capture adsorption trends in Pt-skin architectures. Compositional screening identifies Pt- and Rh-rich subsurface as optimal, with minimal Pd and moderate Ru content which is consistent with prior noble metal HEA screening studies. The predicted adsorption trends follow expectations from d-band theory, providing additional physical validation of the model predictions.