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Space-time thermodynamics and subsystem observables in a kinetically constrained model of glassy materials
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10.1063/1.2374885
/content/aip/journal/jcp/125/18/10.1063/1.2374885
http://aip.metastore.ingenta.com/content/aip/journal/jcp/125/18/10.1063/1.2374885
View: Figures

Figures

Image of FIG. 1.
FIG. 1.

Illustration of a trajectory in a facilitated model with space-time “bubbles” of the inactive state. The boxes illustrate finite observation space-time windows: the top one corresponds to a typical region; the bottom one is a rare collective fluctuation of size much larger than those typical of the active state. On the right are trajectories from the one-spin facilitated one-dimensional Fredrickson-Andersen model (Ref. 10), at for observation windows of and (smaller observation windows of are also outlined).

Image of FIG. 2.
FIG. 2.

Sketch illustrating the effect of the choice of initial condition in a model of diffusing excitations that branch and coalesce. An initial state with no excitations (left) persists throughout the observation time. All other initial conditions result in the system exploring the active steady state (right).

Image of FIG. 3.
FIG. 3.

Distribution of trajectory magnetization at , , and various observation times. We use which is large enough so that does not depend on . The exponential tails of all have similar gradients: the dotted lines are with .

Image of FIG. 4.
FIG. 4.

We show at for varying and . We use which is large enough that the results do not depend on . (Top) Increasing observation time at fixed . (Middle) Increasing box size at . As or is increased, we move from a regime in which the tail gradient is independent of the increasing parameter to a regime in which the gradient is proportional to that parameter. (Bottom) We show a typical trajectory for large and where the observation box is outlined: the size of the total spatial region shown is . For large the trajectories are of the form shown in Fig. 1.

Image of FIG. 5.
FIG. 5.

Data showing (approximate) scaling of in the FA model at various temperatures, scaled according to (9). We plot : the box sizes are ; the observation times are ; and we use . These temperatures are not very small, so there are subleading corrections to scaling, but there is no qualitative change to the scaled distribution on lowering the temperature. Further, the computational time required at is quite significant, so we cannot rule out small systematic errors arising from nonconvergence of our TPS procedure (see Appendix).

Image of FIG. 6.
FIG. 6.

Plot of at , , and , showing secondary maximum at small . (Inset) Enlargement of the secondary peak, shown in a linear scale for .

Image of FIG. 7.
FIG. 7.

Distribution of (reduced) box magnetization in the FA and AA models. The reduced variable , where is the variance of the instantaneous magnetization. Parameters are , , and ; in the FA model ; in the AA model is given by (11) with . For the AA model, is close to Gaussian. The standard deviation is not trivially related to the variance of the box magnetization, so the fact that the Gaussian parts of the two distributions are very similar is a nontrivial consequence of the exact mapping between the two models.

Image of FIG. 8.
FIG. 8.

Distribution of the action in the FA model for , , obtained with . (Top) Contour plot of the joint probability distribution for action density and magnetization (obtained from independent trajectories). The contours are at . The dotted line is the prediction (34). (Bottom) Distribution of the action density [where ].

Image of FIG. 9.
FIG. 9.

(Left) Action distribution in the ensemble with finite . The distribution at is that of Fig. 8 and is shown with symbols. To get data at we simply use (36) and rescale by a constant for convenience (these data are shown as simple lines). (Right) Action distribution with varying at , , and . For we use to ensure that data are independent of . The behavior at small is qualitatively similar to the behavior at small in that the gradient of the exponential tail decreases; at larger a secondary minimum appears. The inset shows an expanded view of the secondary minimum that is present at . Samples with the action exactly equal to zero are omitted from the plot: the probability of this happening is of the order of 1% at .

Image of FIG. 10.
FIG. 10.

Sketch of the steady state density in the generalized model, as a function of , for different values of with . The axis is the FA model and the axis is a line of critical points. The dotted line separates the region in which the scaling of directed percolation (DP) will apply from those in which can be treated perturbatively [so the scaling will be that of the coagulation-diffusion (CD) fixed point]. The FA model is the unique case for which the critical scaling is coagulation diffusion; for finite the relevant critical point is DP.

Image of FIG. 11.
FIG. 11.

Sample trajectories at with conditions otherwise similar to Fig. 3 (, , and ). (Left) Sample from center of distribution. (Right) Sample with . Clearly there are more large inactive regions in these trajectories than in those of Fig. 1; increasing from zero leads to proliferation of large “bubbles.”

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/content/aip/journal/jcp/125/18/10.1063/1.2374885
2006-11-14
2014-04-16
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Space-time thermodynamics and subsystem observables in a kinetically constrained model of glassy materials
http://aip.metastore.ingenta.com/content/aip/journal/jcp/125/18/10.1063/1.2374885
10.1063/1.2374885
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