Machine learning-assisted advances and perspectives for electrolytes of protonic solid oxide fuel cells

Protonic solid oxide fuel cells (P-SOFCs) as a promising power generation technology, have garnered increasing attention due to their advantages of cleanliness, high efficiency, and high reliability. As a critical component of P-SOFCs, proton-conducting electrolytes exhibit high ionic conductivity, enabling high chemical-to-electrical energy conversion efficiency at intermediate temperatures. However, there are still many challenges in further enhancing the proton conductivity and stability of the currently widely used Ba(Zr, Ce)O₃ electrolytes through traditional experimental methods. Herein, this review firstly summarized the current research status of proton-conducting oxides, including ABO₃ perovskite-type oxides and other structural oxides, and highlighted the challenges faced by electrolyte development in terms of proton conductivity, compatibility with other components, and long-term durability. And then, the relevant progresses of machine learning (ML) in the research of P-SOFC electrolytes were meticulously discussed and the promising applications of ML in proton-conducting electrolyte performance screening, stability prediction, and morphology analysis were pointed out. More importantly, the challenges and solutions of proton-conducting electrolytes designed by ML were uncovered by considering the reliable database, feature engineering, accurate model, and experimental validation. Overall, this review concluded the advances of ML-assisted P-SOFC electrolytes and addressed the future research directions in the synergy of ML and electrolyte.