Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs 0.4 La 0.6 Mn 0.25 Co 0.75 O 3 , Cs 0.3 La 0.7 NiO 3 , SrNi 0.75 Co 0.25 O 3 , and Sr 0.25 Ba 0.75 NiO 3 , are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.

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The sluggish kinetics of the oxygen evolution reaction (OER) limits the commercialization of oxygen electrochemistry, which plays a key role in renewable energy technologies such as fuel cells and electrolyzers. Herein, a facile and practical strategy is developed to successfully incorporate Ir single atoms into the lattice of transition metal oxides (TMOs). The chemical environment of Ir and its neighboring lattice oxygen is modulated, and the lattice oxygen provides lone‐pair electrons and charge balance to stabilize Ir single atoms, resulting in the enhancement of both OER activity and durability. In particular, Ir0.08Co2.92O4 NWs exhibit an excellent mass activity of 1343.1 A g−1 and turnover frequency (TOF) of 0.04 s−1 at overpotentials of 300 mV. And this catalyst also displays significant stability in acid at 10 mA cm−2 over 100 h. Overall water splitting using Pt/C as the hydrogen evolution reaction catalyst and Ir0.08Co2.92O4 NWs as the OER catalyst takes only a cell voltage of 1.494 V to achieve 10 mA cm−2 with a perfect stability. This work demonstrates a simple approach to produce highly active and acid‐stable transition metal oxides electrocatalysts with trace Ir.

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