Estimating Reaction Barriers with Deep Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning approach to efficiently estimate minimum energy barriers between stable states in complex systems, addressing the challenge of rare transition events in high-dimensional energy landscapes.

Contribution

It formulates the barrier estimation as a cost-minimization problem and applies reinforcement learning to improve sampling of transition pathways.

Findings

Reinforcement learning effectively identifies minimum energy barriers.

The method improves sampling efficiency for rare transition events.

Potential to enhance understanding of complex system dynamics.

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.