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Finding the hidden structure in glassy systems

Photo by Quino Al on Unsplash

The process of glass formation—the transition from liquid to amorphous solid—has been known for centuries, and glasses have occupied an indispensable place in our lives ever since. Despite this abundance, however, the physics of glass formation remains notoriously poorly understood. Recently a promising new first-principles theory of the glass transition has been developed, but the corresponding equations are extremely computationally demanding to solve. In this project, a deep neural network has been trained to accurately reproduce the solutions of this theory within milliseconds. This orders-of-magnitude speedup suggests that machine-learning methods can provide a powerful alternative to complex numerical integration algorithms associated with complex physical theories.

Participating organisations

Eindhoven University of Technology
Natural Sciences & Engineering
Natural Sciences & Engineering
Netherlands eScience Center

Impact

Output

Team

LJ
Liesbeth Janssen
Lead Applicant
Eindhoven University of Technology
CS
Cornelis Storm
co-Applicant
Eindhoven University of Technology
SC
Simone Ciarella
co-Applicant
Eindhoven University of Technology
CL
Chengjie Luo
PhD Student
Eindhoven University of Technology
CL
Corentin Laudicina
PhD Student
Eindhoven University of Technology
IP
Ilian Pihlajamaa
PhD Student
Eindhoven University of Technology
Jisk Attema
RSE, Programme Manager
Netherlands eScience Center
Meiert Grootes
Meiert Grootes
Sonja Georgievska
Sonja Georgievska