Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis

Abstract

Multi-metal oxides in general and perovskite oxides in particular have attracted considerable attention as oxygen evolution electrocatalysts. Although numerous theoretical studies have been undertaken, the most promising perovskite-based catalysts continue to emerge from human-driven experimental campaigns rather than data-driven machine learning protocols, which are often limited by the scarcity of experimental data on which to train the models. This work promises to break this impasse by demonstrating that active learning on even small datasets—but supplemented by informative structural-characterization data and coupled with closed-loop experimentation—can yield materials of outstanding performance. The model we develop not only reproduces several non-obvious and actively studied experimental trends but also identifies a composition of a perovskite oxide electrocatalyst exhibiting an intrinsic overpotential at 10 mA cm–2oxide of 391 mV, which is among the lowest known of four-metal perovskite oxides. Multi-metal and perovskite oxides are attractive as oxygen evolution electrocatalysts, and thus far the most promising candidates have emerged from experimental methodologies. Active-learning models supplemented by structural-characterization data and closed-loop experimentation can now identify a perovskite oxide with outstanding performance.