Leveraging DFT Calculations and Machine Learning toward Materials Innovations for Proton Ceramic Fuel Cells (PCFCs): A Comprehensive Review

ABSTRACT Protonic ceramic fuel cells (PCFCs) represent a significant advancement in fuel cell technology due to their ability to operate at intermediate temperatures, offering enhanced efficiency and reduced material degradation compared to traditional high‐temperature oxygen ion conducting solid oxide fuel cells (O‐SOFCs). While PCFCs hold immense potential for commercialization, their material innovation remains a critical bottleneck to achieving widespread viability. This comprehensive review explores the synergistic integration of density functional theory (DFT) based calculations with machine learning (ML) methodologies, illuminating their collective impact on accelerating PCFC material development. Combination of DFT's atomic‐level precision in material property prediction with ML's sophisticated predictive algorithms, creates a powerful framework for exploring vast compositional spaces of critical materials, including perovskite oxides, double perovskite oxides, Ruddlesden‐Popper oxides, and other similar systems. The integration not only enhances computational efficiency but also enables the systematic investigation of complex structure–property relationships essential for advancing PCFC technology. The review methodically examines three interconnected themes: First, it delves into the cutting‐edge strategies and material developments that have propelled recent advances in PCFC applications; second, it analyzes DFT's pivotal role in facilitating PCFC progress through accurate atomic‐scale modeling; and third, it elucidates the revolutionary impact of ML integration with DFT methodologies and its implications for PCFC developments. By focusing on seminal contributions within each domain, this work provides a strategic perspective on the convergence of computational chemistry and ML in PCFC's future advancements.