Issue 4, 2024

ChemGymRL: A customizable interactive framework for reinforcement learning for digital chemistry

Abstract

This paper provides a simulated laboratory for making use of reinforcement learning (RL) for material design, synthesis, and discovery. Since RL is fairly data intensive, training agents ‘on-the-fly’ by taking actions in the real world is infeasible and possibly dangerous. Moreover, chemical processing and discovery involves challenges which are not commonly found in RL benchmarks and therefore offer a rich space to work in. We introduce a set of highly customizable and open-source RL environments, ChemGymRL, implementing the standard gymnasium API. ChemGymRL supports a series of interconnected virtual chemical benches where RL agents can operate and train. The paper introduces and details each of these benches using well-known chemical reactions as illustrative examples, and trains a set of standard RL algorithms in each of these benches. Finally, discussion and comparison of the performances of several standard RL methods are provided in addition to a list of directions for future work as a vision for the further development and usage of ChemGymRL.

Graphical abstract: ChemGymRL: A customizable interactive framework for reinforcement learning for digital chemistry

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Article information

Article type
Paper
Submitted
14 Sep 2023
Accepted
13 Feb 2024
First published
20 Feb 2024
This article is Open Access
Creative Commons BY license

Digital Discovery, 2024,3, 742-758

ChemGymRL: A customizable interactive framework for reinforcement learning for digital chemistry

C. Beeler, S. G. Subramanian, K. Sprague, M. Baula, N. Chatti, A. Dawit, X. Li, N. Paquin, M. Shahen, Z. Yang, C. Bellinger, M. Crowley and I. Tamblyn, Digital Discovery, 2024, 3, 742 DOI: 10.1039/D3DD00183K

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