ChemGymRL: A customizable interactive framework for reinforcement learning for digital chemistry-Reference-Cited by-同舟云学术

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

Published:2024 Issue:4 Volume:3 Page:742-758
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Beeler Chris¹²³,Subramanian Sriram Ganapathi²⁴,Sprague Kyle²,Baula Mark²,Chatti Nouha²,Dawit Amanuel²,Li Xinkai²,Paquin Nicholas²,Shahen Mitchell²,Yang Zihan²,Bellinger Colin³,Crowley Mark²^ORCID,Tamblyn Isaac⁴⁵

Affiliation:

1. Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON, Canada

2. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada

3. Digital Technologies, National Research Council of Canada, Ottawa, ON, Canada

4. Vector Institute for Artificial Intelligence, Toronto, ON, Canada

5. Department of Physics, University of Ottawa, Ottawa, ON, Canada

Abstract

Demonstration of a new open source Python library for simulating chemistry experiments as a gymnasium-API, reinforcement learning environment. Allowing learning policies for material design tasks or pipelines using a modular, extendable design.

Funder

National Research Council Canada

Natural Sciences and Engineering Research Council of Canada

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D3DD00183K

Reference91 articles.

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4. Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

5. Materials Acceleration Platforms: On the way to autonomous experimentation

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