Probing reaction channels via reinforcement learning
Senwei Liang Aditya N. Singh Yuanran Zhu David T. Limmer Chao Yang
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.