Design and Analysis of Metal Oxides for CO 2 Reduction Using Machine Learning, Transfer Learning, and Bayesian Optimization.

We aim to achieve resource recycling by capturing and using CO 2 generated in a chemical production and disposal process. We focused on CO 2 conversion to CO by the reverse water gas shift-chemical looping (RWGS-CL) reaction. This reaction proceeds in two steps (H 2 + MO x ⇆ H 2 O + MO x -1 ; CO 2 + MO x -1 ⇆ CO + MO x ) via a metal oxide that acts as an oxygen carrier. High CO 2 conversion can be achieved owing to a low H 2 O concentration in the second step, which causes an unwanted back reaction (H 2 + CO 2 ⇆ CO + H 2 O). However, the RWGS-CL process is difficult to control because of repeated thermochemical redox cycling, and the CO 2 and H 2 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 CO 2 and H 2 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.