Solid oxide electrolysis technologies: Progress, challenges, and perspectives in materials engineering and machine learning approaches

Solid oxide electrolysis cells (SOECs) are promising high-temperature electrochemical devices for efficient hydrogen production, particularly when coupled with renewable electricity and available thermal energy. Their practical deployment, however, remains limited by durability losses arising from electrode degradation, electrolyte/electrode interfacial reactions, interconnect oxidation and chromium volatilization, contaminant poisoning, and thermomechanical mismatch. This review critically examines recent progress in SOEC technologies, cell architectures, constituent materials, and degradation mechanisms, with particular attention to the relationships between material composition, microstructure, ionic/electronic transport, catalytic activity, and long-term stability. Materials-engineering approaches for mitigating degradation are systematically discussed, including doping, infiltration, exsolution, composite electrodes, medium- and high-entropy oxides, barrier layers, interfacial design, and protective strategies for chromium-tolerant interconnects and electrodes. In parallel, the emerging role of machine learning (ML) in solid oxide cell research is evaluated across four principal applications: property prediction, diagnostics, performance prediction, and optimization. Recent studies demonstrate the potential of data-driven models to accelerate candidate selection, interpret structure-property relationships, predict hydrogen production, and support lifetime assessment from electrochemical diagnostic data. Despite substantial advances, the transition to reliable industrial SOEC systems requires standardized long-duration testing, broader and higher-quality datasets, validation under realistic operating conditions, and stronger integration between material development, diagnostics, predictive modelling, and stack-level design. This review provides a materials- and data-driven perspective for advancing efficient, durable, and scalable SOEC technologies.