Automated material synthesis using deep-reinforcement learning


Reinforcement learning (RL) is researched extensively in game playing and has found important uses in finance, autonomous driving and commercial robotics. This project looks at the potential for RL to automate aspects of physical chemistry through gamification to discover new pathways for creating materials with desired properties. This project is a collaboration between the National Research Council (NRC) and University of Waterloo with the NRC providing expertise in physical chemistry and University of Waterloo providing expertise in AI and machine learning.

Project team

Dr. Mark Crowley

Dr. Mark Crowley is an assistant professor in the Pattern Recognition and Machine Intelligence group in the Department of Electrical and Computer Engineering at the University of Waterloo. Dr. Crowley's research focusses on algorithms, tools and theory at the intersection of machine learning, optimization and probabilistic modelling. 

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Dr. Isaac Tamblyn

Dr. Isaac Tamblyn is a Research Officer at the National Research Council and a Vector Institute Faculty Affiliate. He is also an adjunct professor of physics at the University of Ottawa. Dr. Tamblyn's current research interests are focussed on the application of AI and deep learning to problems in nanoscience, in particular materials and processes related to renewable energy.

Find out more about Dr. Tamblyn.