Transfer learning guided discovery of efficient perovskite oxide for alkaline water oxidation

Chang Jiang, Hongyuan He, Hongquan Guo, Xiaoxin Zhang, Qingyang Han, Yanhong Weng, Xianzhu Fu, Yinlong Zhu, Ning Yan, Xin Tu () and Yifei Sun ()
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Chang Jiang: Xiamen University
Hongyuan He: University of Liverpool
Hongquan Guo: Xiamen University
Xiaoxin Zhang: Xiamen University
Qingyang Han: Xiamen University
Yanhong Weng: Shenzhen University
Xianzhu Fu: Shenzhen University
Yinlong Zhu: Nanjing University of Aeronautics and Astronautics
Ning Yan: Wuhan University
Xin Tu: University of Liverpool
Yifei Sun: Xiamen University

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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. Pr0.1Sr0.9Co0.5Fe0.5O3 and Pr0.1Sr0.9Co0.5Fe0.3Mn0.2O3 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. Pr0.1Sr0.9Co0.5Fe0.3Mn0.2O3 demonstrates enhanced OH- affinity compared to Pr0.1Sr0.9Co0.5Fe0.5O3, with the emergence of oxo-bridged Mn-Co conjugate facilitating charge redistribution and dynamic reversibility of Olattice/VO, thereby slowing down Co dissolution. This work paves the way for accelerated discovery and development of high-performance perovskite oxide electrocatalysts for this reaction.

Date: 2024
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DOI: 10.1038/s41467-024-50605-5

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