Evan Reed, Stanford (CMU MSE)

Who: Evan Reed, Stanford University
Friday, September 17, 2021 - 12:15pm

Identification of new battery chemistries guided by data science and multi-metric performance objectives
Zoom link will be provided

Abstract: I will discuss our efforts to combine diverse data sets and materials property calculations with physics and intuition to identify new battery chemistries that satisfy a spectrum of desirable performance metrics. Traditional approaches to battery chemistry development involve the identification of one battery component and optimization of one or two of the desired properties of that component. This approach has been a barrier to the development of new batteries, which are systems of multiple interacting materials that require the simultaneous satisfaction of perhaps a dozen performance metrics and interfacial compatibility conditions. I will discuss our efforts to develop holistic screening techniques to identify promising solid electrolytes and cathodes that satisfy the full spectrum of desired properties, discovered through data science screening approaches, physics informed machine learning, and density functional theory simulations. Specifically, we have identified several new low cost sulfur based electrolytes that are superior to known sulfur electrolytes and have potential to be scaled up in manufacturing. We also identify several cathodes that are more compatible with solid electrolytes than currently studied cathodes.

Bio: Evan Reed is a faculty member in Materials Science and Engineering at Stanford University. He received a B.S. in applied physics from Caltech (1998) and PhD. in physics from MIT (2003). In 2004, he was an E. O. Lawrence Fellow and staff scientist at Lawrence Livermore National Laboratory before moving to Stanford in 2010. Evan Reed’s recent work focuses on atomic scale theory and modeling of 2D and other electronic materials, statistical learning for chemical and energy storage applications, structural phase changes, and high pressure shock wave compression. His group has pioneered the application of data science and machine learning approaches to materials selection problems within these application domains