Title: Prediction and learning for designing materials for solar energy conversion
Presenter(s): Rob Coridan
Date: February 23, 2022
Materials for converting solar energy into chemical fuels requires balancing the many physical and chemical steps involved (light absorption, catalysis, product separation, diffusion of reactants and products) along multiple length scales (from single atoms to m2). This balance requires rational design of solar fuels materials to maximize the energy conversion efficiency. Here, we will discuss the use of large-scale simulations and experimental data to develop machine learning-based approaches to the characterization and design of materials for solar fuels applications.
Dr. Rob Coridan is an Associate Professor in the Department of Chemistry and Biochemistry at the University of Arkansas in Fayetteville. Prof. Coridan earned his Ph.D. in Physics in 2009 at the University of Illinois, Urbana-Champaign, and was a postdoctoral researcher at the UCLA and the California Institute of Technology. He is also an affiliate of the Materials Science and Engineering Program. He joined the University in 2015, where his group develops methods to synthesize and characterize materials for photoelectrochemical and electrocatalytic energy conversion.