%0 Unpublished work
%T Roadmap on machine learning glassy liquids
%+ Laboratoire Charles Coulomb (L2C)
%+ Universiteit Utrecht / Utrecht University [Utrecht]
%+ Google DeepMind
%+ Università degli studi di Trieste = University of Trieste
%+ TAckling the Underspecified (TAU)
%+ University of Pennsylvania
%+ University of Hyogo
%+ Göteborgs Universitet = University of Gothenburg (GU)
%+ Laboratoire de physique de l'ENS - ENS Paris (LPENS)
%+ Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris)
%+ Systèmes Désordonnés et Applications
%A Jung, Gerhard
%A Alkemade, Rinske M
%A Bapst, Victor
%A Coslovich, Daniele
%A Filion, Laura
%A Landes, François, P.
%A Liu, Andrea
%A Pezzicoli, Francesco Saverio
%A Shiba, Hayato
%A Volpe, Giovanni
%A Zamponi, Francesco
%A Berthier, Ludovic
%A Biroli, Giulio
%8 2023-12-14
%D 2023
%Z 2311.14752
%Z Physics [physics]/Condensed Matter [cond-mat]Preprints, Working Papers, ...
%X Unraveling the connections between microscopic structure, emergent physical properties, and slow dynamics has long been a challenge in the field of the glass transition. The absence of clear visible structural order in amorphous configurations complicates the identification of the key features related to structural relaxation and transport properties. The difficulty in sampling equilibrated configurations at low temperatures hampers thorough numerical and theoretical investigations. This roadmap article explores the potential of machine learning (ML) techniques to face these challenges, building on the algorithms that have revolutionized computer vision and image recognition. We present successful ML applications, as well as many open problems for the future, such as transferability and interpretability of ML approaches. We highlight new ideas and directions in which ML could provide breakthroughs to better understand glassy liquids. To foster a collaborative community effort, the article introduces the "GlassBench" dataset, providing simulation data and benchmarks for both two-dimensional and three-dimensional glass-formers. Emphasizing the importance of benchmarks, we identify critical metrics for comparing the performance of emerging ML methodologies, in line with benchmarking practices in image and text recognition. The goal of this roadmap is to provide guidelines for the development of ML techniques in systems displaying slow dynamics, while inspiring new directions to improve our understanding of glassy liquids.
%G English
%L hal-04344224
%U https://hal.science/hal-04344224
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