Plenary speakers

Martin Z. Bazant

Departments of Chemical Engineering and Mathematics
Massachusetts Institute of Technology, USA
Learning Battery Nonequilibrium Thermodynamics from Images
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.

Udo Seifert

II Institute for Theoretical Physics, University of Stuttgart, Germany

Stochastic Thermodynamics and the Thermodynamic Uncertainty Relation
Stochastic thermodynamics provides a universal framework for analyzing nano- and micro-sized non-equilibrium systems. Prominent examples are single molecules, molecular machines, colloidal particles in time-dependent laser traps and biochemical networks. Thermodynamic notions like work, heat and entropy can be identified on the level of individual fluctuating trajectories. They obey universal relations like the fluctuation theorem. Thermodynamic inference as a general strategy uses consistency constraints derived from stochastic thermodynamics to infer otherwise hidden properties of non-equilibrium systems. As its most prominent paradigm, the thermodynamic uncertainty relation provides a lower bound on the entropy production through measurements of the dispersion of any current in the system. Likewise, it yields a model-free bound on the thermodynamic efficiency of molecular motors and quantifies the cost of temporal precision at finite temperature.

Udo Seifert is full professor of theoretical physics at the University of Stuttgart since 2001. In 1989, he graduated at the Ludwig-Maximilians University in Munich under the supervision of Herbert Wagner. After post-doc positions at the Simon Fraser University in British Columbia and at the Research Center in Juelich, he became tenured group leader at the Max-Planck Institut for Colloids and Interfaces in Potsdam in 1994. Within the last fifteen years, he has made essential contributions to the foundations and applications of stochastic thermodynamics.

Hugo Touchette

Department of Mathematical Sciences, Stellenbosch University, South Africa

Large Deviations and Statistical Physics
Probability distributions arising in equilibrium statistical physics are always exponential in the number of particles or volume of the system considered. This property is known in mathematics as the large deviation principle and underlies many fundamental results in thermodynamics and equilibrium statistical physics - most importantly the fact that thermodynamic potentials are related by Legendre transform.
I will give in this talk an introduction to the theory of large deviations, which deals with probabilities having an exponential form, and of its applications in equilibrium and nonequilibrium statistical physics. In the first part of the talk, I will discuss the basics of this theory and its historical sources, which can be traced back in mathematics to Cramer (1938) and Sanov (1960) and, on the physics side, to Einstein (1910) and Boltzmann (1877). In the second part, I will show how the theory can be applied to study equilibrium and nonequilibrium systems and to express many key concepts about these systems in a clear and unified way.

Hugo Touchette obtained his PhD from McGill University in Montreal, Canada, where is from. He moved to the Department of Mathematical Sciences of Stellenbosch University in 2019 after spending 7 years as Chief Researcher at the National Institute for Theoretical Physics (NITheP) in Stellenbosch and, prior to moving to South Africa, 9 years at Queen Mary University of London, UK. He started working on large deviations during his PhD (2004) on nonconcave entropies and the equivalence of ensembles for equilibrium systems. Since then, he has expanded his interest to nonequilibrium systems, publishing many results extending the notions of statistical ensembles and phase transitions to those systems, as well as numerical algorithms to calculate large deviation functions.