Optimization of Performance at Air Electrode Side for Protonic Solid Oxide Cells: Advances and Machine Learning Guided Perspectives

Protonic solid oxide cell (P-SOC) is a novel type of solid oxide cell for hydrogen production and power generation. P-SOCs have garnered significant attention due to their advantages, such as the elimination of precious metals and high conversion efficiency. However, the commercialization of P-SOCs is currently hindered by suboptimal electrochemical performance, particularly at the air electrode side, where challenges in catalytic activity and ionic/electronic conductivity persist. Recently, strategies for designing advanced triple-conducting oxides, exsolution, and optimizing the air electrode-electrolyte interfaces have been proposed to improve the electrochemical reactive area, kinetics, and durability of air electrodes. Thereinto, machine learning (ML) techniques have emerged as powerful tools, playing a crucial role in the above topics. Despite these advancements, a comprehensive review synthesizing these innovative strategies and ML-guided advances and perspectives has been lacking in literature. This review comprehensively makes a summary of these methods and discusses their effects on cell performance. Importantly, the ML-guided perspectives and challenges in accelerating the optimization of these strategies and P-SOCs are proposed here. This paper not only offers valuable insights for understanding and optimizing performances at the air electrode side but also provides a roadmap for the rational design of superior air electrodes of P-SOCs.