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(8) Production(s) de l'année 2024
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Direct Numerical Analysis of Dynamic Facilitation in Glass-Forming Liquids
Auteur(s): Herrero C., Berthier L.
(Article) Publié:
Physical Review Letters, vol. 132 p.258201 (2024)
Texte intégral en Openaccess :
Ref HAL: hal-04623148_v1
Ref Arxiv: 2310.16935
DOI: 10.1103/PhysRevLett.132.258201
Ref. & Cit.: NASA ADS
Exporter : BibTex | endNote
Résumé: We propose a computational strategy to quantify the temperature evolution of the timescales and length scales over which dynamic facilitation affects the relaxation dynamics of glass-forming liquids at low temperatures, which requires no assumption about the nature of the dynamics. In two glass models, we find that dynamic facilitation depends strongly on temperature, leading to a subdiffusive spreading of relaxation events which we characterize using a temperature-dependent dynamic exponent. We also establish that this temperature evolution represents a major contribution to the increase of the structural relaxation time.
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Transverse forces and glassy liquids in infinite dimensions
Auteur(s): Ghimenti Federico, Berthier L., Szamel G., van Wijland Frédéric
(Article) Publié:
Physical Review E, vol. 109 p.064133 (2024)
Texte intégral en Openaccess :
Ref HAL: hal-04615103_v1
Ref Arxiv: 2402.10856
DOI: 10.1103/PhysRevE.109.064133
Ref. & Cit.: NASA ADS
Exporter : BibTex | endNote
Résumé: We explore the dynamics of a simple liquid whose particles, in addition to standard potential-based interactions, are also subjected to transverse forces preserving the Boltzmann distribution. We derive the effective dynamics of one and two tracer particles in the infinite-dimensional limit. We determine the amount of acceleration of the dynamics caused by the transverse forces, in particular in the vicinity of the glass transition. We analyze the emergence and evolution of odd transport phenomena induced by the transverse forces.
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From creep to flow: Granular materials under cyclic shear
Auteur(s): Yuan Ye, Zeng Zhikun, Yuan Houfei, Zhang Shuyang, Kob W., Wang Yujie
(Article) Publié:
Nature Communications, vol. 15 p.3866 (2024)
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Creating equilibrium glassy states via random particle bonding
Auteur(s): Ozawa M., Barrat Jean‐louis, Kob W., Zamponi Francesco
(Article) Publié:
Journal Of Statistical Mechanics: Theory And Experiment, vol. p.013303 (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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