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Roadmap on machine learning glassy liquids 
Auteur(s): Jung G., Alkemade Rinske M, Bapst Victor, Coslovich D., Filion Laura, Landes François, Liu Andrea, Pezzicoli Francesco Saverio, Shiba Hayato, Volpe Giovanni, Zamponi Francesco, Berthier L., Biroli Giulio
(Article) Publié:
-Nature Rev.phys., vol. 7 p.91-104 (2025)
Texte intégral en Openaccess : 
Ref HAL: hal-04344224_v1
Ref Arxiv: 2311.14752
Ref INSPIRE: 2726911
DOI: 10.1038/s42254-024-00791-4
Ref. & Cit.: NASA ADS
Exporter : BibTex | endNote
Résumé: 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.
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