MSE Colloquium: Manos Kioupakis, Predictive Modeling of Electronic Materials for Efficient Energy Conversion

Assistant Professor, Department of Materials Science and Engineering, University of Michigan

Abstract

Modern first-principles calculations based on density functional and many-body perturbation theory enable us to study the electronic and optical properties of materials with predictive accuracy. In this talk, I will discuss the results of our predictive calculations for light-emitting diodes, solar cells, and thermoelectric waste-heat recovery applications. In particular, I will discuss how Auger recombination affects the efficiency of nitride LEDs at high power, and how nitride nanostructures can enable the development of novel optoelectronic devices in the visible and ultraviolet ranges. Moreover, I will discuss how nanostructuring enhances the optical absorption of nanoporous silicon in the visible range with potential applications for the development of thin-film silicon solar cells. Last, I will present resent results we obtained for the thermoelectric conversion efficiency of semiconducting compounds for waste-heat recovery applications. Our results highlight the importance of predictive calculations for the design and optimization of new materials for energy applications.

Bio

Manos Kioupakis is an Assistant Professor in Materials Science and Engineering at the University of Michigan. He obtained his PhD in Physics in 2008 from the University of California, Berkeley and from 2008-2011 he was a post-doctoral scholar at the University of California, Santa Barbara. He develops and applies first-principles computational methods for the predictive modeling of electronic and optoelectronic materials and devices. He has received the NSF CAREER award in 2013 and the Jon R. and Beverly S. Holt Award for Excellence in Teaching in 2014.