Design and Analysis of Metal Oxides for CO₂ Reduction Using Machine Learning, Transfer Learning, and Bayesian Optimization

We aim to achieve resource recycling by capturing and using CO₂ generated in a chemical production and disposal process. We focused on CO₂ conversion to CO by the reverse water gas shift-chemical looping (RWGS-CL) reaction. This reaction proceeds in two steps (H₂ + MO _x ⇆ H₂O + MO _x-1; CO₂ + MO _x-1 ⇆ CO + MO _x ) via a metal oxide that acts as an oxygen carrier. High CO₂ conversion can be achieved owing to a low H₂O concentration in the second step, which causes an unwanted back reaction (H₂ + CO₂ ⇆ CO + H₂O). However, the RWGS-CL process is difficult to control because of repeated thermochemical redox cycling, and the CO₂ and H₂ conversion extents vary depending on the metal oxide composition and experimental conditions. In this study, we developed metal oxides and simultaneously optimized experimental conditions to satisfy target CO₂ and H₂ conversion extents by using machine learning and Bayesian optimization. We used transfer learning to improve the prediction accuracy of the mathematical models by incorporating a data set and knowledge of oxygen vacancy formation energy. Furthermore, we analyzed the RWGS-CL reaction based on the prediction accuracy of each variable and the feature importance of the random forest regression model.