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In recent years, deep learning has made remarkable strides, surpassing humancapabilities in tasks like strategy games, and it has found applications incomplex domains, including protein folding. In the realm of quantum chemistry,machine learning methods have primarily served as predictive tools or designaids using generative models, while reinforcement learning remains in its earlystages of exploration. This work introduces an actor-critic reinforcementlearning framework suitable for diverse optimization tasks, such as searchingfor molecular structures with specific properties within conformational spaces.As an example, we show an implementation of this scheme for calculating minimumenergy pathways of a Claisen rearrangement reaction and a number of SN2reactions. Our results show that the algorithm is able to accurately predictminimum energy pathways and thus, transition states, therefore providing thefirst steps in using actor-critic methods to study chemical reactions.

Reinforcement learning for traversing chemical structure space: Optimizing transition states and minimum energy paths of molecules