LBan-1

Overview

This lesson is structured in three parts:

• Self-averaging and disorder in statistical systems

Disordered systems are characterized by a random energy landscape, however, in the thermodynamic limit, physical observables become deterministic. This property, known as self-averaging, does not always hold for the partition function which is the quantity that we can compute. When it holds the annealed average $\ln {\overline {Z}}$ and the quenched average $\ln Z$ coincides otherwiese we have

$\ln Z<\ln {\overline {Z}}$

• The Random Energy Model

We study the Random Energy Model (REM) introduced by Bernard Derrida. In this model at each configuration is assigned an independent energy drawn from a Gaussian distribution of extensive variance. The model exhibits a freezing transition at a critical temperature​, below which the free energy becomes dominated by the lowest energy states.

• Extreme value statistics and saddle-point analysis

The results obtained from a saddle-point approximation can be recovered using the tools of extreme value statistics.

Part I

Random energy landascape

In a system with $N$ degrees of freedom, the number of configurations grows exponentially with $N$. For simplicity, consider Ising spins that take two values, $\sigma _{i}=\pm 1$, located on a lattice of size $L$ in $d$ dimensions. In this case, $N=L^{d}$ and the number of configurations is $M=2^{N}=e^{N\log 2}$.

In the presence of disorder, the energy associated with a given configuration becomes a random quantity. For instance, in the Edwards-Anderson model:

$E=-\sum _{\langle i,j\rangle }J_{ij}\sigma _{i}\sigma _{j},$

where the sum runs over nearest neighbors $\langle i,j\rangle$, and the couplings $J_{ij}$ are independent and identically distributed (i.i.d.) Gaussian random variables with zero mean and unit variance.

The energy of a given configuration is a random quantity because each system corresponds to a different realization of the disorder. In an experiment, this means that each of us has a different physical sample; in a numerical simulation, it means that each of us has generated a different set of couplings $J_{ij}$.

To illustrate this, consider a single configuration, for example the one where all spins are up. The energy of this configuration is given by the sum of all the couplings between neighboring spins:

$E[\sigma _{1}=1,\sigma _{2}=1,\ldots ]=-\sum _{\langle i,j\rangle }J_{ij}.$

Since the the couplings are random, the energy associated with this particular configuration is itself a Gaussian random variable, with zero mean and a variance proportional to the number of terms in the sum — that is, of order $N$. The same reasoning applies to each of the $M=2^{N}$ configurations. So, in a disordered system, the entire energy landscape is random and sample-dependent.

Self-averaging observables

A crucial question is whether the macroscopic properties measured on a given sample are themselves random or not. Our everyday experience suggests that they are not: materials like glass, ceramics, or bronze have well-defined, reproducible physical properties that can be reliably controlled for industrial applications.

From a more mathematical point of view, it means that the free energy $F_{N}(\beta )=Nf_{N}(\beta )$ and its derivatives (magnetization, specific heat, susceptibility, etc.), in the limit $N\to \infty$, these random quantities concentrates around a well defined value. These observables are called self-averaging. This means that,

$\lim _{N\to \infty }f_{N}=\lim _{N\to \infty }f_{N}^{\text{typ}}=\lim _{N\to \infty }{\overline {f_{N}}}=f_{\infty }$

Hence $f_{N}$ becomes effectively deterministic and its sample-to sample fluctuations vanish in relative terms:

$\lim _{N\to \infty }{\frac {\overline {f_{N}^{2}}}{{\overline {f_{N}}}^{2}}}=1.$

The partition function

The partition function

$Z_{N}=\exp(-\beta Nf_{N})$

is itself a random variable in disordered systems. Analytical methods can capture the statistical properties of this variable. We can define to average over the disorder realizations:

• The annealed average corresponds to the calculation of the moments of the partition function. The annealed free energy is

$f^{\text{ann.}}-{\frac {1}{\beta N}}\ln \,{\overline {Z_{N}}}$

• the quenched average corresponds to the average of the logarithm of the partition function, which is self-averaging for sure.

$f_{\infty }\sim {\overline {f_{N}}}=-\ln Z_{N}/(\beta N)$

Do these two averages coincide?

If the partition function is self-averaging in the thermodynamic limit, then

$\lim _{N\to \infty }Z_{N}=\lim _{N\to \infty }Z_{N}^{\text{typ}}=\lim _{N\to \infty }{\overline {Z_{N}}}=e^{-\beta Nf_{\infty }}$

As a consequence, the annealed and the quenched averages coincide.

If the partition function is not self-averaging, only typical partition function concentrates, but extremely rare configurations contribute disproportionately to its moments:

$\lim _{N\to \infty }Z_{N}^{\text{typ}}=e^{-\beta Nf_{\infty }}<\lim _{N\to \infty }{\overline {Z_{N}}}=e^{-\beta Nf^{\text{ann.}}}$

There are then two main strategies to determine the deterministic value of the observable :

• Compute directly the quenched average using methods such as the replica trick and the Parisi solution.

• Determine the typical value $Z_{N}^{\text{typ}}$ and evaluate $f_{\infty }=-{\frac {1}{\beta N}}\ln Z_{N}^{\text{typ}}$

Part II

Random Energy Model

This model simplifies the problem by neglecting correlations between the $M=2^{N}$ configurations and assuming that the energies $E_{\alpha }$ are(i.i.d.) Gaussian variables with zero mean and variance $E_{N}$.

Number of states is given by:

$p(E_{\alpha })={\frac {1}{\sqrt {2\pi N}}}\exp \left(-{\frac {E_{\alpha }^{2}}{2N}}\right)$