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Symbolic regression (SR) is an emerging method for building analyticalformulas to find models that best fit data sets. Here, SR was used to guide thedesign of new oxide perovskite catalysts with improved oxygen evolutionreaction (OER) activities. An unprecedentedly simple descriptor, {\mu}/t, where{\mu} and t are the octahedral and tolerance factors, respectively, wasidentified, which accelerated the discovery of a series of new oxide perovskitecatalysts with improved OER activity. We successfully synthesized five newoxide perovskites and characterized their OER activities. Remarkably, four ofthem, Cs0.4La0.6Mn0.25Co0.75O3, Cs0.3La0.7NiO3, SrNi0.75Co0.25O3, andSr0.25Ba0.75NiO3, outperform the current state-of-the-art oxide perovskitecatalyst, Ba0.5Sr0.5Co0.8Fe0.2O3 (BSCF). Our results demonstrate the potentialof SR for accelerating data-driven design and discovery of new materials withimproved properties.

Symbolic Regression Discovery of New Perovskite Catalysts with High Oxygen Evolution Reaction Activity