Automated Materials Design and Discovery Using Reinforcement Learning

Studying how to automate material synthesis and discovery by training a Deep Reinforcement Learning system to plan and carry out chemical synthesis experiments to gather data and find efficient pathways to making new or known materials.

DOMAINS | ai-for-material-design | digital-chemistry | ai-for-physics | ai-for-science

WEBPAGE: | https://uwaterloo.ca/scholar/mcrowley/dblstudy

In early 2019 the lab began a new collaboration sponsored by the National Research Council – UW Collaboration Centre (NUCC) on AI/Cybersecurity/IoT. This is a new organization set-up to initial research collaboration between NRC staff researchers and UW PIs. I am one of the first faculty to be a part of this endeavour and to receive funding for my work. With Dr. Isaac Tamblyn (NRC) our project studies how to automate material synthesis and discovery by training a Deep Reinforcement Learning system to plan and carry out chemical synthesis experiments to gather data and find efficient pathways to making new or known materials.

Go take a look at the current framework to carry out your own experiments: chemgymrl.com

Our Papers on ChemGymRL

  1. Amrl
    Active Measure Reinforcement Learning for Observation Cost Minimization: A framework for minimizing measurement costs in reinforcement learning
    Bellinger, Colin, Coles, Rory, Crowley, Mark, and Tamblyn, Isaac
    In Canadian Conference on Artificial Intelligence 2021
  2. Reinforcement Learning in a Physics-Inspired Semi-Markov Environment
    Bellinger, Colin, Coles, Rory, Crowley, Mark, and Tamblyn, Isaac
    In Canadian Conference on Artificial Intelligence 2020