Comparative Analysis of Reinforcement Learning Algorithms for Finding Reaction Pathways: Insights from a Large...

Area of Science:

• Computational Chemistry
• Chemical Reaction Engineering
• Machine Learning Applications

Background:

• Identifying kinetically feasible reaction pathways is vital for predicting chemical reactions and understanding mechanisms.
• Complexity and scale in molecular systems increase the challenge of mapping reaction pathways, even with advanced computational methods.

Purpose of the Study:

• To develop and validate a reinforcement learning (RL) algorithm for efficiently identifying kinetically feasible reaction pathways.
• To compare the performance of various RL search policies for reaction pathway discovery.

Main Methods:

• Implementation of a reinforcement learning algorithm to search for reaction pathways between reactant and product structures.
• Validation using a benchmark dataset of large-scale chemical reaction path networks.
• Proposal and evaluation of several search policies, including greedy, random, uniform, and exploration-exploitation balanced methods (Thompson sampling, probability of improvement, expected improvement).

Main Results:

• Reinforcement learning algorithm successfully identified kinetically feasible reaction pathways.
• Exploration-exploitation balanced policies demonstrated consistently stable and high performance, outperforming baseline policies.
• Detailed characterization of search mechanisms influenced by different policies was achieved.

Conclusions:

• The developed RL algorithm offers an efficient approach for mapping chemical reaction pathways.
• Balanced exploration-exploitation strategies are crucial for robust performance in complex chemical systems.
• Future research directions include hierarchical RL and multiobjective optimization for enhanced pathway discovery.