Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userWhat makes a complex liquid complex?
274
0
0.0
(
0
)
Added by
Alexander Patashinski
Publication date
2012
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
We view a complex liquid as a network of bonds connecting each particle to its nearest neighbors; the dynamics of this network is a chain of discrete events signaling particles rearrangements. Within this picture, we studied a two-dimensional complex liquid and found a stretched-exponential decay of the network memory and a power-law for the distribution of the times for which a particle keeps its nearest neighbors; the dependence of this distribution on temperature suggests a possible dynamical critical point. We identified and quantified the underlying spatio-temporal phenomena. The equilibrium liquid represents a hierarchical structure, a mosaic of long-living crystallites partially separated by less-ordered regions. The long-time dynamics of this structure is dominated by particles redistribution between dynamically and structurally different regions. We argue that these are generic features of locally ordered but globally disordered complex systems. In particular, these features must be taken into account by any coarse-grained theory of dynamics of complex fluids and glasses.
rate research
Read More
We study transient behaviour in the dynamics of complex systems described by a set of non-linear ODEs. Destabilizing nature of transient trajectories is discussed and its connection with the eigenvalue-based linearization procedure. The complexity is realized as a random matrix drawn from a modified May-Wigner model. Based on the initial response of the system, we identify a novel stable-transient regime. We calculate exact abundances of typical and extreme transient trajectories finding both Gaussian and Tracy-Widom distributions known in extreme value statistics. We identify degrees of freedom driving transient behaviour as connected to the eigenvectors and encoded in a non-orthogonality matrix $T_0$. We accordingly extend the May-Wigner model to contain a phase with typical transient trajectories present. An exact norm of the trajectory is obtained in the vanishing $T_0$ limit where it describes a normal matrix.
We consider a class of random block matrix models in $d$ dimensions, $d ge 1$, motivated by the study of the vibrational density of states (DOS) of soft spheres near the isostatic point. The contact networks of average degree $Z = z_0 + zeta$ are represented by random $z_0$-regular graphs (only the circle graph in $d=1$ with $z_0=2$) to which Erdos-Renyi graphs having a small average degree $zeta$ are superimposed. In the case $d=1$, for $zeta$ small the shifted Kesten-McKay DOS with parameter $Z$ is a mean-field solution for the DOS. Numerical simulations in the $z_0=2$ model, which is the $k=1$ Newman-Watts small-world model, and in the $z_0=3$ model lead us to conjecture that for $zeta to 0$ the cumulative function of the DOS converges uniformly to that of the shifted Kesten-McKay DOS, in an interval $[0, omega_0]$, with $omega_0 < sqrt{z_0-1} + 1$. For $2 le d le 4$, we introduce a cutoff parameter $K_d le 0.5$ modeling sphere repulsion. The case $K_d=0$ is the random elastic network case, with the DOS close to the Marchenko-Pastur DOS with parameter $t=frac{Z}{d}$. For $K_d$ large the DOS is close for small $omega$ to the shifted Kesten-McKay DOS with parameter $t=frac{Z}{d}$; in the isostatic case the DOS has around $omega=0$ the expected plateau. The boson peak frequency in $d=3$ with $K_3$ large is close to the one found in molecular dynamics simulations for $Z=7$ and $8$.
We use a simple model to extend network models for activated dynamics to a continuous landscape with a well-defined notion of distance and a direct connection to many-body systems. The model consists of a tracer in a high-dimensional funnel landscape with no disorder. We find a non-equilibrium low-temperature phase with aging dynamics, that is effectively equivalent to that of models with built-in disorder, such as Trap Model, Step Model and REM.
In this chapter we discuss how the results developed within the theory of fractals and Self-Organized Criticality (SOC) can be fruitfully exploited as ingredients of adaptive network models. In order to maintain the presentation self-contained, we first review the basic ideas behind fractal theory and SOC. We then briefly review some results in the field of complex networks, and some of the models that have been proposed. Finally, we present a self-organized model recently proposed by Garlaschelli et al. [Nat. Phys. 3, 813 (2007)] that couples the fitness network model defined by Caldarelli et al. [Phys. Rev. Lett. 89, 258702 (2002)] with the evolution model proposed by Bak and Sneppen [Phys. Rev. Lett. 71, 4083 (1993)] as a prototype of SOC. Remarkably, we show that the results obtained for the two models separately change dramatically when they are coupled together. This indicates that self-organized networks may represent an entirely novel class of complex systems, whose properties cannot be straightforwardly understood in terms of what we have learnt so far.
We derive the spectral properties of adjacency matrix of complex networks and of their Laplacian by the replica method combined with a dynamical population algorithm. By assuming the order parameter to be a product of Gaussian distributions, the present theory provides a solution for the non linear integral equations for the spectra density in random matrix theory of the spectra of sparse random matrices making a step forward with respect to the effective medium approximation (EMA) . We extend these results also to weighted networks with weight-degree correlations
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
