Probing reaction channels via reinforcement learning
This work addresses the challenge of understanding reaction mechanisms in chemistry, particularly for high-dimensional systems, though it appears incremental as it builds on existing neural network and reinforcement learning techniques.
The authors tackled the problem of identifying key configurations along chemical reaction paths by proposing a reinforcement learning method to generate an ensemble of configurations, which was used to approximate the committor function for evaluating reaction rates, even in high-dimensional settings.
We propose a reinforcement learning based method to identify important configurations that connect reactant and product states along chemical reaction paths. By shooting multiple trajectories from these configurations, we can generate an ensemble of configurations that concentrate on the transition path ensemble. This configuration ensemble can be effectively employed in a neural network-based partial differential equation solver to obtain an approximation solution of a restricted Backward Kolmogorov equation, even when the dimension of the problem is very high. The resulting solution, known as the committor function, encodes mechanistic information for the reaction and can in turn be used to evaluate reaction rates.