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Solid oxide electrolysis cells (SOECs) represent a promising technology because they have the potential to achieve greater efficiency than existing electrolysis methods, making them a strong candidate for sustainable hydrogen production. SOECs utilize a solid oxide electrolyte, which facilitates the migration of oxygen ions while maintaining gas impermeability at temperatures between 600 °C and 900 °C. This review provides an overview of the recent advancements in research and development at the intersection of machine learning and SOECs technology. It emphasizes how data-driven methods can improve performance prediction, facilitate material discovery, and enhance operational efficiency, with a particular focus on materials for cathode-supported cells. This paper also addresses the challenges associated with implementing machine learning for SOECs, such as data scarcity and the need for robust validation techniques. This paper aims to address challenges related to material degradation and the intricate electrochemical behaviors observed in SOECs. It provides a description of the reactions that may be involved in the degradation mechanisms, taking into account thermodynamic and kinetic factors. This information is utilized to construct a fault tree, which helps categorize various faults and enhances understanding of the relationship between their causes and symptoms.
Abadi et al. (Sun,) studied this question.