Spatiotemporal Hierarchy of Relaxation Events, Dynamical Heterogeneities, and Structural Reorganization in a Supercooled Liquid

Candelier, Raphaël; Widmer‐Cooper, Asaph; Kummerfeld, Jonathan K.; Dauchot, Olivier; Biroli, Giulio; Harrowell, Peter; Reichman, David R.

doi:10.1103/physrevlett.105.135702

Cited by 174 publications

(183 citation statements)

References 21 publications

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“…In disordered solids, it has long been hypothesized that localized particle rearrangements [3] induced by stress or temperature also occur at localized flow defects [4][5][6]. Like dislocations in crystals [7], flow defects in disordered solids are particularly effective in scattering sound waves, so analyses of the low-frequency vibrational modes [8] have been used successfully to demonstrate the existence of localized flow defects [7,[9][10][11][12][13][14][15][16][17]. However, all attempts to identify flow defects [18,19] directly from the structure, without using knowledge of the interparticle interactions, have failed [18,19].…”

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confidence: 99%

Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods

et al. 2015

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We use machine learning methods on local structure to identify flow defects -or regions susceptible to rearrangement -in jammed and glassy systems. We apply this method successfully to two disparate systems: a two dimensional experimental realization of a granular pillar under compression, and a Lennard-Jones glass in both two and three dimensions above and below its glass transition temperature. We also identify characteristics of flow defects that differentiate them from the rest of the sample. Our results show it is possible to discern subtle structural features responsible for heterogeneous dynamics observed across a broad range of disordered materials.

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confidence: 99%

Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods

et al. 2015

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“…[25][26][27] Later it was demonstrated that the real space properties of the inherent structure normal modes correlate closely with dynamical heterogeneities and irreversible configurational rearrangements. [28][29][30][31] Such connections have also been demonstrated in experiments performed on colloidal suspensions. [32][33][34][35] It remains unclear in the examples discussed above if the harmonic soft modes are an active player in the dynamics or if they are spectators whose positions correlate with regions of dynamical activity but which do not influence particle relaxation.…”

Section: Introductionmentioning

confidence: 64%

A small subset of normal modes mimics the properties of dynamical heterogeneity in a model supercooled liquid

Hocky

Reichman

2013

The Journal of Chemical Physics

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In this work, we study the nature of transitions between inherent structures of a two-dimensional model supercooled liquid. We demonstrate that these transitions occur predominately along a small number of directions on the energy landscape. Moreover, we show that the number of such directions decreases as the temperature of the liquid is decreased in the mildly supercooled regime, in concert with earlier studies on an athermal jamming system. We show that this decrease happens in parallel with a change in character of the transitions as dynamics in the system become more heterogeneous and localized. We investigate the origin of these trends, which suggests interesting connections between jamming and thermal glassy phenomena.

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“…Once the timescales are properly rescaled, the similarities with the granular media experiments are astonishing [34,48].…”

Section: Spatio-temporal Organization Of the Relaxation Close To The mentioning

confidence: 99%

“…In the first section, we describe the microscopic mechanisms at play in granular systems approaching the glass transition and emphasize the decreasing role of facilitation associated to the emergence of large-scale dynamical heterogeneities [34,35,36]. In the second section, we experimentally study the vicinity of the Jamming transition by investigating both statics and dynamics of the contact network in a horizontally shaken bi-disperse packing of brass or photo-elastic discs.…”

Section: Introductionmentioning

confidence: 99%

The glass and jamming transitions in dense granular matter

Coulais¹,

Dauchot²

2013

AIP Conference Proceedings

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Abstract. Everyday life tells us that matter acquires rigidity either when it cools down, like lava flows which turn into solid rocks, or when it is compacted, like tablets simply formed by powder compression. As suggested by these examples, solidification is not the sole privilege of crystals but also happens for disordered media such as glass formers, granular media, foams, emulsions and colloidal suspensions. Fifteen years ago the "Jamming paradigm" emerged to encompass in a unique framework the glass transition and the emergence of yield stress, two challenging issues in modern condensed matter physics. One must realize how bold this proposal was, given that the glass transition is a finite temperature transition governing the dynamical properties of supercooled liquids, while Jamming is essentially a zero temperature, zero external stress and purely geometric transition which occurs when a given packing of particles reaches the maximum compression state above which particles start to overlap. More recently, the observation of remarkable scaling properties on the approach to jamming led to the conjecture that this zero temperature "critical point" could determine the properties of dense particle systems within a region of the parameter space to be determined, which in principle could include thermal and stressed systems. Fifteen years of intense theoretical and experimental work later, what have we learned about Jamming and glassy dynamics ? In this paper, we discuss these issues in the light of the experiments we have been conducting with vibrated grains.

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Spatiotemporal Hierarchy of Relaxation Events, Dynamical Heterogeneities, and Structural Reorganization in a Supercooled Liquid

Cited by 174 publications

References 21 publications

Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods

Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods

A small subset of normal modes mimics the properties of dynamical heterogeneity in a model supercooled liquid

The glass and jamming transitions in dense granular matter

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