Machine Learning-Based Predictive Modeling for Solid Oxide Electrolysis Cell (SOEC) Electrochemical Performance

Solid oxide electrolysis cells (SOECs) are emerging as a promising technology for high-efficiency and environmentally friendly hydrogen production. While laboratory-scale experiments and physics-based simulations have significantly advanced SOEC research, there remains a need for faster, scalable, and cost-effective methods to predict electrochemical performance. This study explores the feasibility of using machine learning (ML) techniques to model the performance of SOECs with the material configuration LSM-YSZ/YSZ/Ni-YSZ. A dataset of 593 records (from 31 IV curves) was compiled from 12 peer-reviewed sources and used to train and evaluate four ML algorithms: SVR, ANN, XGBoost, and Random Forest. Among these, XGBoost achieved the highest accuracy, with an R2 of 98.39% for cell voltage prediction and 98.10% for IV curve interpolation test under typical conditions. Extrapolation tests revealed the model’s limitations in generalizing beyond the bounds of the training data, emphasizing the importance of comprehensive data coverage. Overall, the results confirm that ML models, particularly XGBoost, can serve as accurate and efficient tools for predicting SOEC electrochemical behavior when applied with appropriate data coverage and guided by materials science concepts.