In turn, Figure (b) shows the Strecker reaction, with 9,203 EQs. The start node, representing four reactant species, is located around 144 kJ/mol, in a lower energy, lesspopulated region, and lies slightly outside the structural similarity peak. The goal node, representing three product species, is at approximately 102 kJ/mol, much closer to the start ii Datasets with energies corrected using a temperature of 300 K are used. An overview of our reinforcement learning algorithm is presented in Figure . In this problem set-up, two EQ nodes, -one corresponding to the reactant and the other corresponding to the product-are initially provided, are designated as the start and goal nodes, respectively. The start node is then added to the pool of node selection candidates for sampling the reaction path, initiating the search process (iteration step t = 1). At each iteration step, the first task is to select one node from the pool of candidates according to the node selection policy, which is constructed and updated based on the similarity scores of the sampled nodes based on their energies and structures in relation to those of the goal node, and the numbers of reaction paths sampled on candidate nodes up to the present iteration step, as detailed in Section 3. Next, one reaction path is sampled from those directly connected to the selected node and one EQ associated with that reaction path is obtained. The reaction path is randomly sampled from the set of reaction path candidates available to the node (as included in the SCAN data). Although reaction path sampling is idealized by randomization, it is practical to set up a problem where the number of reaction path candidates is unknown in advance and the same reaction path can be sampled in duplicate. If the obtained EQ is different from those previously acquired and the time constant of the sampled reaction path is below a specified threshold (τ max = 1 day), the obtained (new) EQ node is added to the pool of node selection candidates for the further sampling of the reaction path. If the sampled reaction path is the same, the selected node is removed from the candidate pool according to a duplicate-path criterion: Suppose that the n th distinct reaction path is sampled for the selected node at a certain step (i.e., by that step, n distinct reaction paths are sampled from that node). Then, suppose that when additional m steps (selections) have been made from the node, but any different reaction path is not found within the m trials, with satisfying (1 -1/(n + 1)) m < ϵ with a confidence level defined by ϵ iii . Then, the selected node will be removed from the candidate pool. This criterion rejects the hypothesis that there exists an (n + 1) th distinct reaction path. Initially, ϵ is set at 1.0, and if the candidate pool becomes empty because of node removal, ϵ is decreased by 0.1. All the nodes removed from the candidate pool up to the present iteration step are re-evaluated using the same criterion, and if they no longer satisfy the removal condition, they are returned to the pool. If the obtained EQ node is the goal node, the search is terminated, the iteration step at which the search stopped (t stop ) is recorded, and iii 1/(n + 1) represents the probability of randomly selecting the new reaction path from a set of (n + 1) path candidates, and (1 -1/(n + 1))