Computational Discovery of Materials for Energy Storage

 

Abstract:

The accuracy of atomic-scale quantum-mechanical simulations has advanced to the point where it is now possible to use these techniques to identify new materials with improved properties. Depending on the property to be predicted — and the computational cost to perform the evaluation — computation can be used to rapidly screen a large number of candidate compositions. This information can guide experiments towards materials that exhibit promise and minimize time investments in developing those that are dead ends. This seminar will describe several examples of computational discovery for energy storage materi-als. Examples include materials for chemical energy storage (hydrogen and natural gas), electrical energy storage (solid state batteries), and thermal energy storage (water sorption in hydrates). Connections to experiments will be highlighted, and coupling to machine learning will be discussed.

 

Biography:

Don Siegel is Professor and Chair of the Walker Department of Mechanical Engineering at the University of Texas at Austin (UT). He is also a Core Faculty Member in the Texas Materials Institute and in the Oden Institute for Computational Engineering and Sciences. Prior to joining UT in 2021, Prof. Siegel spent 12 years as a faculty member in the Department of Mechanical Engineering at the University of Michigan (UM). Prior to joining UM, he was a Technical Expert at Ford Research and Advanced Engi-neering in Dearborn, MI.

Siegel is a computational materials scientist whose research targets the development of energy storage materials and lightweight alloys. A physicist by training, Prof. Siegel received a Ph.D. from the University of Illinois at Urbana-Champaign. His postdoctoral training was performed at Sandia National Laboratories and at the U.S. Naval Research Lab. He was a VELUX Visiting Professor in the Department of Energy Conversion and Storage at the Technical University of Denmark during the 2015-2016 academic year.