Home

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 prevent time investments in developing those that are dead ends). This seminar will describe several examples of computational discovery for energy storage materials. 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 AI will be discussed.

Bio

Don Siegel is Professor and Chair of the Walker Department of Mechanical Engineering at the University of Texas at Austin. At UT he is a Temple Foundation Endowed Professor and holds a Cockrell Family Chair for Departmental Leadership. Prior to joining UT in 2021, Prof. Siegel spent 12 years as a faculty member at the University of Michigan. Siegel is a computational materials scientist whose research targets the development of energy storage materials and lightweight alloys. He is a recipient of the NSF Career Award and a Gilbreth Lectureship from the National Academy of Engineering.

Refreshments provided after the speaker