Abstract
The increasing demand for sustainable energy has prompted extensive investigations into electrochemical conversion devices such as fuel cells, water splitting systems, and metal-air batteries. The efficiency of bifunctional oxygen electrodes plays a crucial role in overall electrochemical performance. In recent times, spinel oxides (AB2O4) have emerged as promising candidates for this purpose; however, the limited prior research emphasizes the necessity for a thorough and exhaustive exploration. This study introduces a computational framework that combines machine learning techniques with density functional theory calculations to systematically screen 1240 spinel oxides. Stability evaluations are guided by geometric relation and crystal-likeness score. A metal/nonmetal classifier based on transfer learning is utilized to address data scarcity, thereby improving prediction accuracy. Several selected candidates exhibit superior performance compared to the benchmarking perovskite oxide. Furthermore, we emphasize their potential as mixed ionic and electronic conductors, featuring a network of ion diffusion pathways. This research establishes design principles for the development of high-performance spinel oxide bifunctional oxygen electrocatalysts.
Published Version
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