Comparative Analysis of Reinforcement Learning Algorithms for Finding Reaction Pathways: Insights from a Large Benchmark Data Set

The identification of kinetically feasible reaction pathways that connect a reactant to its product, including numerous intermediates and transition states, is crucial for predicting chemical reactions and elucidating reaction mechanisms. However, as molecular systems become increasingly complex or larger, the number of local minimum structures and transition states grows, which makes this task challenging, even with advanced computational approaches. We introduced a reinforcement learning algorithm to efficiently identify a kinetically feasible reaction pathway between a given local minimum structure for the reactant and a given one for the product, starting from the reactant. The performance of the algorithm was validated using a benchmark data set of large-scale chemical reaction path networks. Several search policies were proposed, using metrics based on energetic or structural similarity to the product's goal structure, for each local minimum structure candidate found during the search. The performances of baseline greedy, random, and uniform search policies varied substantially depending on the system. In contrast, exploration-exploitation balanced policies such as Thompson sampling, probability of improvement, and expected improvement consistently demonstrated stable and high performance. Furthermore, we characterized the search mechanisms that depend on different policies in detail. This study also addressed potential avenues for further research, such as hierarchical reinforcement learning and multiobjective optimization, which could deepen the problem setting explored in this study.