Estimating Reaction Barriers with Deep Reinforcement Learning
This addresses the problem of rare event sampling in high-dimensional systems for researchers in computational chemistry or physics, but it appears incremental as it applies an existing method (reinforcement learning) to a known bottleneck.
The paper tackles the challenge of determining transition pathways between stable states in complex systems by formulating it as a cost-minimization problem and using reinforcement learning algorithms to efficiently sample and find minimum energy barriers.
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.