Exploring Bayesian Optimization for Photocatalytic Reduction of CO2

Abstract

The optimization of photocatalysis is complex, as heterogenous catalysis makes its kinetic modeling or design of experiment (DOE) significantly more difficult than homogeneous reactions. On the other hand, Bayesian optimization (BO) has been found to be efficient in the optimization of many complex chemical problems but has rarely been studied in photocatalysis. In this paper, we developed a BO platform and applied it to the optimization of three photocatalytic CO2 reduction systems that have been kinetically modeled in previous studies. Three decision variables, namely, partial pressure of CO2, partial pressure of H2O, and reaction time, were used to optimize the reaction rate. We first compared BO with the traditional DOE methods in the Khalilzadeh and Tan systems and found that the optimized reaction rates predicted by BO were 0.7% and 11.0% higher, respectively, than the best results of optimization by DOE, and were significantly better than the original experimental data, which were 1.9% and 13.6% higher, respectively. In both systems, we also explored the best combination of the surrogate model and acquisition function for BO, and the results showed that the combination of Gaussian processes (GP) and upper confidence bound (UCB) had the most stable search performance. Furthermore, the Thompson system with time dependence was optimized with BO according to the selectivity of CH4. The results showed that the optimized reaction time of BO agreed with the actual experimental data with an error of less than 5%. These results suggest that BO is a more promising alternative to kinetic modeling or traditional DOE in the efficient optimization of photocatalytic reduction.