Traditional methods of scientific inquiry and engineering design begin with human intelligence: Mathematical models encoding physical hypotheses are proposed, tested against experimental data and refined by fitting 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 useful models of nonequilibrium thermodynamics, in the particular context of Li-ion batteries. Examples of learning physics from image data include inferring electro-autocatalytic reaction models from x-ray diffraction spectra for NMC oxides, optical videos of lithium metal growth on graphite, and x-ray adsorption imaging of driven phase separation in LFP, as well as inversion of impedance spectra to determine microstructural heterogeneity, and 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. A major focus of his research is on the nonequilibrium thermodynamics of electrochemical systems. 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. He also serves as the Chief Scientific Advisor for Saint Gobain Research North America in Northborough, MA.