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Physique Statistique
(33) Production(s) de l'année 2024
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Emerging Mesoscale Flows and Chaotic Advection in Dense Active Matter 
Auteur(s): Keta Y.-E., Klamser J., Jack Robert, Berthier L.
(Article) Publié:
Physical Review Letters, vol. 132 p.218301 (2024)
Texte intégral en Openaccess : 
Ref HAL: hal-04603641_v1
Ref Arxiv: 2306.07172
DOI: 10.1103/PhysRevLett.132.218301
Ref. & Cit.: NASA ADS
Exporter : BibTex | endNote
Résumé: We study two models of overdamped self-propelled disks in two dimensions, with and without aligning interactions. Both models support active mesoscale flows, leading to chaotic advection and transport over large length scales in their homogeneous dense fluid states, away from dynamical arrest. They form streams and vortices reminiscent of multiscale flow patterns in turbulence. We show that the characteristics of these flows do not depend on the specific details of the active fluids, and result from the competition between crowding effects and persistent propulsions. This observation suggests that dense active suspensions of self-propelled particles present a type of “active turbulence” distinct from collective flows reported in other types of active systems. Published by the American Physical Society 2024
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Dynamic heterogeneity at the experimental glass transition predicted by transferable machine learning
Auteur(s): Jung G., Biroli Giulio, Berthier L.
(Article) Publié:
Physical Review B, vol. 109 p.064205 (2024)
Texte intégral en Openaccess :
Ref HAL: hal-04514863_v1
Ref Arxiv: 2310.20252
DOI: 10.1103/PhysRevB.109.064205
Ref. & Cit.: NASA ADS
Exporter : BibTex | endNote
Résumé: We develop a machine learning model, which predicts structural relaxation from amorphous supercooled liquid structures. The trained networks are able to predict dynamic heterogeneity across a broad range of temperatures and time scales with excellent accuracy and transferability. We use the network transferability to predict dynamic heterogeneity down to the experimental glass transition temperature Tg, where structural relaxation cannot be analyzed using molecular dynamics simulations. The results indicate that the strength, the geometry, and the characteristic length scale of the dynamic heterogeneity evolve much more slowly near Tg compared to their evolution at higher temperatures. Our results show that machine learning techniques can provide physical insights on the nature of the glass transition that cannot be gained using conventional simulation techniques.
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Elaboration of a neural-network interatomic potential for silica glass and melt
Auteur(s): Trillot Salomé, Lam Julien, Ispas S., Kandy Akshay Krishna Ammothum, Tuckerman Mark, Tarrat Nathalie, Benoit Magali
(Article) Publié:
Computational Materials Science, vol. 236 p.112848 (2024)
Texte intégral en Openaccess : 
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Collective Relaxation Dynamics in a Three-Dimensional Lattice Glass Model
Auteur(s): Nishikawa Y., Berthier L.
(Article) Publié:
Physical Review Letters, vol. 132 p.067101 (2024)
Texte intégral en Openaccess :
Ref HAL: hal-04453684_v1
Ref Arxiv: 2307.08110
DOI: 10.1103/PhysRevLett.132.067101
Ref. & Cit.: NASA ADS
Exporter : BibTex | endNote
Résumé: We numerically elucidate the microscopic mechanisms controlling the relaxation dynamics of a three-dimensional lattice glass model that has static properties compatible with the approach to a random first-order transition. At low temperatures, the relaxation is triggered by a small population of particles with low-energy barriers forming mobile clusters. These emerging quasiparticles act as facilitating defects responsible for the spatially heterogeneous dynamics of the system, whose characteristic lengthscales remain strongly coupled to thermodynamic fluctuations. We compare our findings both with existing theoretical models and atomistic simulations of glass-formers.
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Simulations informatiques de la transition vitreuse et des matériaux vitreux
Auteur(s): Barrat Jean-Louis, Berthier L.
(Article) Publié:
Comptes Rendus Physique, vol. 24 p.57-72 (2024)
Texte intégral en Openaccess :
Ref HAL: hal-04086013_v1
Ref Arxiv: 2206.01013
DOI: 10.5802/crphys.129
Ref. & Cit.: NASA ADS
Exporter : BibTex | endNote
Résumé: Nous présentons une vue d’ensemble des différents types de techniques de calcul développées au fil des ans pour étudier les liquides surfondus, les matériaux vitreux et la physique de la transition vitreuse. Nous organisons ces stratégies numériques en quatre grandes familles. Pour chacune d’entre elles, nous décrivons les idées générales sans discuter des détails techniques. Nous résumons le type de questions qui peuvent être abordées par une approche donnée et décrivons les principaux résultats qui ont été obtenus. Enfin, nous décrivons deux directions importantes pour les futures études informatiques des systèmes vitreux.
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