LAUSR

Summary High-entropy alloys (HEAs) provide a near-continuous distribution of adsorption energies. With a minority of sites having optimal properties that dominate the catalysis, the overall catalytic activity can increase. In this article, we focus on the oxygen reduction reaction (ORR). We present density functional theory (DFT) calculated *OH and *O adsorption energies on a random subset of available binding sites on the surface of the HEA IrPdPtRhRu. Employing a simple machine learning algorithm, we predict remaining adsorption energies, finding good agreement between calculated and predicted values. With a full catalog of available adsorption energies, an appropriate expression for predicting catalytic activity was used to optimize the HEA composition. The HEA then becomes a design platform for the unbiased discovery of new alloys by promoting sites with exceptional catalytic activity. Setting different optimization constraints led to a new HEA composition and binary alloy IrPt, showing significant enhancements over pure Pt(111).