Time & Date: 2:00PM Feb 25, 2022
Location: Zoom Link Passcode: 209579
In collaboration with Clean Energy Research Centre and China-Canada Bioenergy Centre
LEARNING BATTERY PHYSICS FROM IMAGES
Traditional methods of theoretical chemical engineering begin with human intelligence: Mathematical models encoding physical hypotheses are proposed, tested against experimental data and refined by fitting a few adjustable parameters. Recent advances in artificial intelligence appear to challenge this paradigm, since predictions can be made directly from data without the need for models, but such knowledge is often not transferrable to new situations. This talk will present a hybrid approach of solving PDE-constrained inverse problems to derive new models of electrochemical nonequilibrium thermodynamics, in the context of Li-ion batteries. Examples include inferring electro- autocatalytic reaction models from x-ray diffraction spectra for nickel-rich oxides, optical videos of lithium metal growth on graphite and scanning tunneling x-ray adsorption images of driven phase separation in lithium iron phosphate, as well as inversion of impedance spectra to infer microstructural heterogeneity and of acoustic emission spectra to reveal degradation processes during battery forming.
MARTIN Z. BAZANT is the E. G. Roos (1944) Chair Professor of Chemical Engineering and Mathematics at the Massachusetts Institute of Technology. His research focuses on transport phenomena, electrochemical systems and applied mathematics. After a Ph.D. in Physics at Harvard (1997), he joined the MIT faculty in Mathematics (1998) and then Chemical Engineering (2008), where he has served as Executive Officer (2016-2020) and as the first Digital Learning Officer (2020-). He was elected Fellow of the American Physical Society, the International Society of Electrochemistry, and the Royal Society of Chemistry, and awarded the 2015 Kuznetsov Prize in Theoretical Electrochemistry (ISE), the 2018 Andreas Acrivos Award for Professional Progress in Chemical Engineering (AIChE), the 2019 MITx Prize for Teaching and Learning in Massive Open Online Courses. Since 2014, he has served as the Chief Scientific Advisor for Saint Gobain Research North America in Northborough, MA.