Constructing New Statistical Physics Theories From Big Data

The glass transition is an infamous example of an emergent collective phenomenon in many-body systems that is stubbornly resistant to microscopic understanding. The transition combines three major theoretical challenges by involving nonlinear responses that occur far from equilibrium in disordered systems. Establishing the connection between microscopic properties and the glass transition requires reducing vast quantities of microscopic information to a few relevant microscopic variables and their distributions.

In this lecture, Andrea Liu will demonstrate how machine learning, designed for dimensional reduction, can provide a natural way forward when standard statistical physics tools fail. She will describe how she and her group have harnessed machine learning to identify an important microscopic quantity for the glass transition — a structural order parameter— from snapshot images. She will then explain how they use that quantity to build a new theory for glassy dynamics and how big data approaches can be applied to other statistical physics problems, including in living systems.

Liu is a theoretical soft and living matter physicist at the University of Pennsylvania, where she is the Hepburn professor of physics and director of the Center for Soft and Living Matter. She has served recently in the speaker line of the Council of the American Physical Society (APS) and chair line of the Physics Section of the American Association for the Advancement of Science (AAAS). Liu is currently on the Committee on Human Rights of the National Academies of Science, Engineering and Medicine. She is a fellow of the APS, AAAS and the American Academy of Arts and Sciences and is a member of the National Academy of Sciences.