Accelerating Combinatorial Electrocatalyst Discovery with Bayesian Optimization: a Case Study in the Quaternary System Ni‐Pd‐Pt‐Ru for the Oxygen Evolution Reaction

The discovery of high-performance electrocatalysts is crucial for advancing sustainable energy technologies. Compositionally complex solid solutions comprising multiple metals offer promising catalytic properties, yet their exploration is challenging due to the combinatorial explosion of possible compositions. In this work, human-decision driven combinatorial sputtering of thin-film materials libraries and high-throughput characterization is combined with Bayesian optimization to efficiently explore the quaternary composition space Ni-Pd-Pt-Ru for the oxygen evolution reaction (OER) in alkaline media. Using this method, the global activity optimum of pure Ru is identified after covering less than 20% of the complete composition space with six materials libraries. Six additional libraries are fabricated to validate the activity trend. The resulting dataset is used to formulate general guidelines for the efficient composition of space exploration with combinatorial synthesis paired with Bayesian optimization.