Discovery of materials for solar thermochemical hydrogen combining machine learning, computational chemistry, experiments and system simulations

This study integrates first-principles calculations, computational chemistry, system simulations, experiments, and machine learning to identify redox perovskite oxides for solar thermochemical hydrogen production. Using two random forest regressions and one classification model, the approach predicts materials’ stability and the enthalpy of oxygen vacancy formation (\(\Delta {h}_{o}\)), a critical property for selecting materials for thermochemical hydrogen production. B-site composition significantly influences \(\Delta {h}_{o}\) predictions. The methodology led to the discovery of Ba_0.875Ca_0.125Zr_0.875Mn_0.125O₃ (BCZM), which reduces at temperatures up to 250 °C lower than CeO₂ and is expected to outperform other perovskites in water splitting. However, CeO₂ remains the benchmark for solar thermochemical hydrogen production. The combined use of machine learning and DFT calculations refined \(\triangle {h}_{o}\) predictions and provided insights into experimental results. This framework not only enhances database creation for material screening but also establishes a novel approach for perovskite discovery for hydrogen production applications.