2014
DOI: 10.1103/physreve.89.032204
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Jamming by shape in kinetically constrained models

Abstract: We derive expressions for the critical density for jamming in a hyper-rhomboid system of arbitrary shape in any dimension for the Kob-Andersen and Fredrickson-Andersen kinetically constrained models. We find that changing the system's shape without altering its total volume or particle density may induce jamming. We also find a transition between shapes in which the correlation length between jammed particles is infinite and shapes that have a finite correlation length.

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Cited by 10 publications
(21 citation statements)
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References 62 publications
(116 reference statements)
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“…the particles are antipersistent). This correction to the base diffusion coefficient is qualitatively similar to the combined effect of persistence and finite density on the MSD, which increases with density for highly anti-persistent walkers [53]. Note that as this is not a gradient model, using the density dependence of the correlation functions at pseudo-equilibrium is only an approximation [58].…”
Section: Effective Diffusion Equationmentioning
confidence: 59%
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“…the particles are antipersistent). This correction to the base diffusion coefficient is qualitatively similar to the combined effect of persistence and finite density on the MSD, which increases with density for highly anti-persistent walkers [53]. Note that as this is not a gradient model, using the density dependence of the correlation functions at pseudo-equilibrium is only an approximation [58].…”
Section: Effective Diffusion Equationmentioning
confidence: 59%
“…In this paper we generalise our previous study [53] and consider the correlations and the hydrodynamic description of random walkers with a general finite memory moving on a d dimensional hypercubic lattice. If the velocity autocorrelations are positive, we call the walkers persistent, while if they are negative we call them anti-persistent.…”
Section: Introductionmentioning
confidence: 99%
“…Extending this algorithm to higher dimensional models with m = 2 is straightforward, since the unfrozen region must be a hyper-rhomboid, 25,26 such that at any stage in the algorithm the only variables which should be kept in memory are the size of the hyper-rhomboid (d variables, with d being the dimension of the system), the number of occupied checked sites in the two layers adjacent to each of the 2d sides (4d variables), and whether the corners are occupied or not (2 d variables), for a total of 2 d + 5d variables. For m ≥ 3, the extension of the algorithm is not trivial, since the unfrozen region does not have to be a hyper-rhomboid.…”
Section: Simulation Algorithmmentioning
confidence: 99%
“…These models can be expanded to higher dimensional hyper-cubic lattices with a general number m of vacant neighbors needed for movement. 25,26 We restrict ourselves in this article to the m = 2 models in a two-dimensional square lattice. Our work can be easily extended to higher dimensional models with a) eialteom@post.tau.ac.il b) shokef@tau.ac.il m = 2, but extending it to models with m ≥ 3 is much more complicated.…”
Section: Introductionmentioning
confidence: 99%
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