Machine learning analysis of gas phase photocatalytic CO2 reduction for hydrogen production

In this work, the gas phase photocatalytic CO 2 reduction was analyzed via machine learning, and the results were compared with those obtained in liquid phase process. Total 549 data points (268 for gas and 281 for liquid phase) were extracted from 80 published papers for this purpose. The general trends in the literature were analyzed using simple descriptive statistics first; then, the random forest (RF) regression was used for the band gap prediction while the decision tree (DT) classification was utilized to deduce heuristics for higher CO 2 reduction rates. It was found that H 2 , CO, and CH 4 are the main products in gas phase while CH 3 OH production is more dominant in liquid phase. Random forest prediction was quite successful in predicting the band gap with the root mean square error of 0.15 for testing. Decision tree models for the total gas production rates were also successful; for example, the accuracy rates for training and testing were 80% and 79% respectively for the gas phase processes. The high precision for the high gas production class allowed to deduce some rules indicating the semiconductor and co-catalysts options for high CO 2 photoreduction rates while the reaction temperatures was also found to be influential in the liquid phase. • Photocatalytic CO 2 reduction was studied using machine learning. • Descriptive statistics, decision tree (DT) and random forest (RF) were used. • The bandgap prediction with RF was quite successful. • Heuristic rules were deduced for high total gas production rate using DT. • Semiconductor and co-catalyst were found to be deterministic.