|About this Abstract
||Materials Science & Technology 2020
||Materials Design through AI Composition and Process Optimization
||High-fidelity Accelerated Design of High-performance Electrochemical Systems
||Rachel C. Kurchin, Alan Edelman, Viral Shah, Chris Rackauckas, Bryce Meredig, Venkat Viswanathan
|On-Site Speaker (Planned)
||Rachel C. Kurchin
Two efforts to curb greenhouse gas emissions involve seeking an electrochemical alternative to the Haber-Bosch process and increasing energy density of lithium-ion batteries to enable electric aviation and trucking. Novel materials can address both these challenges; however, evaluations of candidate material systems in-silico and experimentally are limited to small design spaces and low-fidelity screening that fail to model realistic operating conditions.
I will present a collaborative effort spanning academia and industry that aims to alter this paradigm by enabling rapid, high-fidelity screening of large numbers of electrochemical functional materials for use in new energy technologies. This approach will be demonstrated for a model problem in electrocatalysis and shown to extend for battery electrolyte design. We are advancing state-of-the-art along several fronts, including neural differential equations and graph convolutional neural networks for fast evaluation, and efficient global optimization over the chemical space. Experimental validation will be done for high-value candidates.