Reinforcement learning for traversing chemical structure space: Optimizing transition states and minimum energy paths of molecules

TL;DR

This paper introduces an actor-critic reinforcement learning framework to optimize and predict transition states and minimum energy paths in molecular structures, advancing the application of RL in quantum chemistry.

Contribution

It presents the first application of actor-critic reinforcement learning to study chemical reactions and energy pathways in molecules.

Findings

Accurately predicts minimum energy pathways and transition states.

Successfully applied to Claisen rearrangement and SN2 reactions.

Demonstrates potential of RL in quantum chemistry optimization tasks.

Abstract

In recent years, deep learning has made remarkable strides, surpassing human capabilities in tasks like strategy games, and it has found applications in complex domains, including protein folding. In the realm of quantum chemistry, machine learning methods have primarily served as predictive tools or design aids using generative models, while reinforcement learning remains in its early stages of exploration. This work introduces an actor-critic reinforcement learning framework suitable for diverse optimization tasks, such as searching for molecular structures with specific properties within conformational spaces. As an example, we show an implementation of this scheme for calculating minimum energy pathways of a Claisen rearrangement reaction and a number of SN2 reactions. Our results show that the algorithm is able to accurately predict minimum energy pathways and thus, transition states, therefore providing the first steps in using actor-critic methods to study chemical reactions.