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