Combining High-throughput and AI to Create Autonomous Synchrotron Experiments

Jason Hattrick-Simpers

Professor, Department of Materials Science and Engineering, University of Toronto, and Research Scientist at Canmet MATERIALS

Since the release of the 2017 Mission Innovation report on Materials Acceleration Platforms there has been intense interest in combining high-throughput experimentation (HTE), artificial intelligence (AI), and materials data to accelerate the rate of new materials discovery. In this talk I will discuss a user’s perspective of what happens when AI+HTE are effectively implemented at a synchrotron beamline. I will use three case studies illustrating the systematic build-up of the 1-5 end station at SLAC from a HTE only platform to a fully autonomous tool capable of being directed remotely. Common themes to emerge will be material and experimentation representation, effective knowledge extraction or analysis minimization, and experimental planning to maximize knowledge from the minimal number of experiments.

Jason Hattrick-Simpers is a Professor at the Department of Materials Science and Engineering, University of Toronto and a Research Scientist at Canmet MATERIALS. He graduated with a B.S. in Mathematics and a B.S. in Physics from Rowan University and a Ph.D. in Materials Science and Engineering from the University of Maryland. Prof. Hattrick-Simpers’ research interests focus on the use of AI and experimental automation to discover new functional alloys and oxides that can survive in extreme environments and materials for energy conversion and storage. Specific topics of interest to the group include corrosion resistant ultra-hard alloys, oxides, nitrides, and carbides; thermoelectric materials for heat to energy conversion; novel metals for hydrogen fueling stations; and oxides for CO2 conversion.

Prior to joining UofT, Prof. Hattrick-Simpers was a staff scientist at the National Institute of Standards and Technology (NIST) in Gaithersburg, MD where he co-developed tools for discovering novel corrosion resistance of alloys, developed active learning approaches to guide thin film and additive manufacturing alloy studies, and developed tools and best practices to enable trust in AI within the materials science community. He has published over 80 papers and given more than 50 invited seminars and talks.  He was an associate editor of ACS Combinatorial Science from 2017 – 2020 and is part of the organizing committee for the International Workshop on Combinatorial Materials Science and Technology.