Workshop: Machine Learning Glassy Dynamics
Amorphous materials such as glasses have been used since prehistoric times, but in recent years their technological applications have rapidly grown. However, despite many decades of research using experiments, theory and computer simulations, fundamental mechanisms such as mechanical properties and stress relaxation in glasses are still poorly understood. In recent years, machine learning techniques have started to be applied to help solve these fundamental questions, creating a new research path to study disordered materials. This workshop is dedicated to understanding the role that machine learning can play in better understanding glasses from a fundamental perspective. The original format should trigger new collaboration work between different actors and help organise this rapidly growing research field.
This conference will be publicly live streamed on the ENS Data Science Youtube channel.
Dates of conference open to all:
- 7-8 November 2022
- Registration deadline: 30 September 2022
Dates of organized discussions for selected participants:
- 9-11 November 2022
- Registration deadline: 30 September 2022
Machine learning glassy dynamics (Andrea Liu, University of Pennsylvania)
The three-dimensional glass transition is an infamous example of an emergent collective phenomenon in many-body systems that is stubbornly resistant to microscopic understanding using traditional statistical physics approaches. 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. I will demonstrate how machine learning, designed for dimensional reduction, can provide a natural way forward when standard statistical physics tools fail. We have harnessed machine learning to identify a useful microscopic structural quantity for the glass transition, have applied it to simulation and experimental data, and have used it to build a new model for glassy dynamics.
Date: Thursday (10. November) 12:45 - 13:45
Venue: Room Jaurès ENS (24 Rue Lhomond) with open buffet afterwards
Organizers
- Ludovic Berthier, CNRS Montpellier
- Giulio Biroli, Ecole Normale Supérieure, Paris
- Gerhard Jung, CNRS Montpellier
Invited Speakers
- Victor Bapst, Google DeepMind
- Daniele Coslovich, University of Trieste
- Olivier Dauchot, ESPCI
- Laura Filion, Utrecht University
- Francois Landes, Université Paris Sud
- Andrea Liu, UPenn
- Sylvain Patinet, ESPCI
- David Richard, Université Grenoble Alpes
- Jorg Rottler, University of British Columbia
- Miguel Ruiz-Garcia, Universidad Carlos III de Madri
- Camille Scalliet, University of Cambridge
- Giovanni Volpe, University of Gothenburg
- Francesco Zamponi, Ecole Normale Supérieure
More information about the workshop: Machine Learning Glassy Dynamics
Workshop Location
- Monday and Tuesday: Collège de France - site Ulm (3 rue d'Ulm - 75005)
- Wednesday to Friday: Centre de Sciences de Données - École Normale Supérieure - (45 rue d’Ulm - 75005)
Registration
- Conference and discussion days registration deadline : 30 September 2022
