Abstract
Perovskite oxides show promise for the oxygen evolution reaction. However, numerical chemical compositions remain unexplored due to inefficient trial-and-error methods for material discovery. Here, we develop a transfer learning paradigm incorporating a pre-trained model, ensemble learning, and active learning, enabling the prediction of undiscovered perovskite oxides with enhanced generalizability for this reaction. Screening 16,050 compositions leads to the identification and synthesis of 36 new perovskite oxides, including 13 pure perovskite structures. Pr(0.1)Sr(0.9)Co(0.5)Fe(0.5)O(3) and Pr(0.1)Sr(0.9)Co(0.5)Fe(0.3)Mn(0.2)O(3) exhibit low overpotentials of 327 mV and 315 mV at 10 mA cm(-2), respectively. Electrochemical measurements reveal coexistence of absorbate evolution and lattice oxygen mechanisms for O-O coupling in both materials. Pr(0.1)Sr(0.9)Co(0.5)Fe(0.3)Mn(0.2)O(3) demonstrates enhanced OH(-) affinity compared to Pr(0.1)Sr(0.9)Co(0.5)Fe(0.5)O(3), with the emergence of oxo-bridged Mn-Co conjugate facilitating charge redistribution and dynamic reversibility of O(lattice)/V(O), thereby slowing down Co dissolution. This work paves the way for accelerated discovery and development of high-performance perovskite oxide electrocatalysts for this reaction.