Machine Learning Seminar Series with Chris Bartel (CEMS, UMN)
Automatic interpretation and control of X-ray diffraction experiments
The emergence of high-throughput quantum chemical calculations has accelerated the rate at which we can predict new materials for various applications (batteries, solar cells, catalysts, etc.), but the successful synthesis of these materials has often become the slow step in materials design.
Autonomous laboratories hold the potential to systematically explore various synthesis routes to new materials, alleviating the painstaking manual trial-and-error approach. However, for an autonomous laboratory to work for inorganic synthesis, we need a method for assessing the success of a given synthesis effort without any human intervention.
Powder X-ray diffraction (XRD) is the workhorse technique for determining the outcome of materials synthesis. In this talk, I will show how convolutional neural networks can be trained to automatically interpret XRD patterns and identify the phases present in realistic mixtures of crystalline phases. I will also discuss how these same models can be leveraged to adaptively control the XRD experiment itself to improve the quality of predictions and enable the detection of short-lived intermediates that form during synthesis.
About Chris Bartel
Chris Bartel joined the Department of Chemical Engineering and Materials Science (CEMS) at the University of Minnesota as an Assistant Professor in August 2022. His research group is using quantum chemical calculations and machine learning to accelerate the discovery and design of solid-state materials for sustainable energy technologies. Prior to his current appointment, he was a postdoc in Materials Science & Engineering at UC Berkeley and Berkeley Lab, working with Prof. Gerd Ceder. He earned his PhD in Chemical Engineering at the University of Colorado Boulder under the supervision of Prof. Al Weimer and Prof. Charles Musgrave. He was the recipient of an NSF Graduate Research Fellowship and the Max S. Peters Outstanding Graduate Award at the University of Colorado.