  Active, selective and stable catalysts are imperative for sustainable energy
conversion, and engineering materials with such properties are highly desired.
High-entropy alloys (HEAs) offer a vast compositional space for tuning such
properties. Too vast, however, to traverse without the proper tools. Here, we
report the use of Bayesian optimization on a model based on density functional
theory (DFT) to predict the most active compositions for the electrochemical
oxygen reduction reaction (ORR) with the least possible number of sampled
compositions for the two HEAs Ag-Ir-Pd-Pt-Ru and Ir-Pd-Pt-Rh-Ru. The discovered
optima are then scrutinized with DFT and subjected to experimental validation
where optimal catalytic activities are verified for Ag-Pd, Ir-Pt, and Pd-Ru
binary systems. This study offers insight into the number of experiments needed
for exploring the vast compositional space of multimetallic alloys which has
been determined to be on the order of 50 for ORR on these HEAs.
