Searching for an Optimal Multi‐Metallic Alloy Catalyst by Active Learning Combined with Experiments

Abstract Searching for an optimal component and composition of multi‐metallic alloy catalysts, comprising two or more elements, is one of the key issues in catalysis research. Due to the exhaustive data requirement of conventional machine‐learning (ML) models and the high cost of experimental trials, current approaches rely mainly on the combination of density functional theory and ML techniques. In this study, a significant step is taken toward overcoming limitations by the interplay of experiment and active learning to effectively search for an optimal component and composition of multi‐metallic alloy catalysts. The active‐learning model is iteratively updated using by examining electrocatalytic performance of fabricated solid‐solution nanoparticles for the hydrogen evolution reaction (HER). An optimal metal precursor composition of Pt 0.65 Ru 0.30 Ni 0.05 exhibits an HER overpotential…