Estimating Reaction Barriers with Deep Reinforcement Learning

Abstract

Stable states in complex systems correspond to local minima on the associated potential energy surface. Transitions between these local minima govern the dynamics of such systems. Precisely determining the transition pathways in complex and high-dimensional systems is challenging because these transitions are rare events, and isolating the relevant species in experiments is difficult. Most of the time, the system remains near a local minimum, with rare, large fluctuations leading to transitions between minima. The probability of such transitions decreases exponentially with the height of the energy barrier, making the system's dynamics highly sensitive to the calculated energy barriers. This work aims to formulate the problem of finding the minimum energy barrier between two stable states in the system's state space as a cost-minimization problem. We propose solving this problem using reinforcement learning algorithms. The exploratory nature of reinforcement learning agents enables efficient sampling and determination of the minimum energy barrier for transitions.

19 pages, 9 figures, 2 tables, updated version

References in corpus (5)

• Nudged elastic band calculations accelerated with Gaussian process regression
• Autonomous artificial intelligence discovers mechanisms of molecular self-organization in virtual experiments
• Deep Reinforcement Learning of Transition States
• Reinforcement learning for traversing chemical structure space: Optimizing transition states and minimum energy paths of molecules
• Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning