L-1

Goal: Spin glass transition. From the experiments with the anomaly on the magnetic susceptibility to order parameter of the transition. We will discuss the arguments linked to extreme value statistics

Spin glass Transition

Spin glass behavior was first observed in experiments with non-magnetic metals (such as Cu, Fe, Au, etc.) doped with a small percentage of magnetic impurities, typically Mn. At low doping levels, the magnetic moments of Mn atoms interact via the Ruderman–Kittel–Kasuya–Yosida (RKKY) interaction. This interaction has a random sign due to the random spatial distribution of Mn atoms within the non-magnetic metal. A freezing temperature, $T_{f}$, separates the high-temperature paramagnetic phase from the low-temperature spin glass phase:

• Above $T_{f}$: The magnetic susceptibility follows the standard Curie law, $\chi (T)\sim 1/T$.
• Below $T_{f}$: Strong metastability emerges, leading to differences between the field-cooled (FC) and zero-field-cooled (ZFC) protocols:

(i) In the ZFC protocol, the susceptibility decreases with decreasing temperature, $T$.

(ii)In the FC protocol, the susceptibility freezes at $T_{f}$, remaining constant at $\chi _{FC}(T<T_{f})=\chi (T_{f})$.

Understanding whether these data reveal a true thermodynamic transition and determining the nature of this new "glassy" phase remains an open challenge to this day. However, in the early 1980s, spin glass models were successfully solved within the mean-field approximation. In this limit, it is possible to determine the phase diagram and demonstrate the existence of a glassy phase where the entropy vanishes at a finite temperature. Furthermore, a condensation of the Gibbs measure onto a few configurations is observed.

Edwards Anderson model

The first significant theoretical attempt to describe spin glasses is the Edwards-Anderson model. For simplicity, we will consider the Ising version of this model.

Ising spins take two values, $\sigma _{i}=\pm 1$, and are located on a lattice with $N$ sites, indexed by $i=1,2,\ldots ,N$. The energy of the system is expressed as a sum over nearest neighbors $\langle i,j\rangle$:

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

Edwards and Anderson proposed studying this model with couplings $J_{ij}$ that are independent and identically distributed (i.i.d.) random variables with a zero mean. The coupling distribution is denoted by $\pi (J)$, and the average over the couplings, referred to as the disorder average, is indicated by an overline:

${\overline {J}}\equiv \int dJ\,J\,\pi (J)=0.$

We will consider two specific coupling distributions:

• Gaussian couplings: $\pi (J)=\exp \left(-J^{2}/2\right)/{\sqrt {2\pi }}$.
• Coin-toss couplings: $J=\pm 1$, chosen with equal probability $1/2$.

Edwards Anderson order parameter

Since ${\overline {J}}=0$, the model does not exhibit spatial magnetic order, such as ferromagnetic or antiferromagnetic order. Instead, the idea is to distinguish between two phases:

• Paramagnetic phase: Configurations are explored with all possible spin orientations.
• Spin glass phase: Spin orientations are random but frozen (i.e., immobile).

The glass phase is characterized by long-range correlations in time, despite the absence of long-range correlations in space. The order parameter for this phase is:

$q_{EA}=\lim _{t\to \infty }\lim _{N\to \infty }{\frac {1}{N}}\sum _{i}\sigma _{i}(0)\sigma _{i}(t),$

where $q_{EA}$ measures the overlap of the spin configuration with itself after a long time.

In the paramagnetic phase, $q_{EA}=0$, while in the spin glass phase, $q_{EA}>0$.

This raises the question of whether the transition at $T_{f}$ is truly thermodynamic in nature. Indeed, in the definition of the Edwards-Anderson (EA) parameter, time seems to play a role, and the magnetic susceptibility does not diverge at the freezing temperature $T_{f}$. The divergence of the magnetic susceptibility in ferromagnets is due to the fact that the magnetization $M=\sum _{i}\sigma _{i}$ serves as the order parameter, distinguishing the ordered and disordered phases. However, in the spin glass model, magnetization is zero in both phases and the order parameter is $q_{EA}$.

It can be shown that the associated susceptibility corresponds to the nonlinear susceptibility:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \frac{M}{H} = \chi + a_3 H^2 + a_5 H^4 + \ldots }

where Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \chi} is the linear susceptibility, and Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle a_3, a_5, \ldots} are higher-order coefficients. Experiments have demonstrated that Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle a_3} and Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle a_5} exhibit singular behavior, providing experimental evidence for the existence of a thermodynamic transition at Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle T_f} .

The SK model

Sherrington and Kirkpatrik considered the fully connected version of the model with Gaussian couplings:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle E= - \sum_{i,j} \frac{J_{ij}}{ \sqrt{N}} \sigma_i \sigma_j }

At the inverse temperature Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \beta } , the partion function of the model is

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle Z= \sum_{\alpha=1}^{2^N} z_{\alpha}, \quad \text{with}\; z_{\alpha}= e^{-\beta E_\alpha} }

Here Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle E_\alpha } is the energy associated to the configuration Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \alpha } . This model presents a thermodynamic transition.

Random energy model

The solution of the SK is difficult. To make progress we first study the radnom energy model (REM) introduced by B. Derrida. This model neglects the correlations between the Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle M=2^N } configurations and assumes the Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle E_{\alpha} } as iid variables.

• Show that the energy distribution is

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle p(E_\alpha) =\frac{1}{\sqrt{2 \pi \sigma_M^2}}e^{-\frac{E_{\alpha}^2}{2 \sigma_M^2}}}

and determine Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \sigma_M^2= N= \log M/\log 2}

We provide different solutions of the Random Energy Model (REM). The first one focus on the statistics of the smallest energies among the ones associated to the Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle M=2^N} configurations. For this, we need to become familiar with the main results of extreme value statistic of iid variables.

Extreme Value Statistics

Consider the Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle M = 2^N} energies Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle E_1, \dots, E_M} as independent and identically distributed (i.i.d.) random variables drawn from a distribution Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle p(E)} (Gaussian in the case of the REM). It is useful to introduce the cumulative probability of finding an energy smaller than E:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P^<(E) = \int_{-\infty}^E dx \, p(x)}

The complementary probability of finding an energy larger than E is:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P^>(E) = \int_E^{+\infty} dx \, p(x) = 1 - P^<(E)}

We define:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle E_{\min} = \min(E_1, \dots, E_M)}

Our goal is to compute the cumulative distribution:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle Q_M(\epsilon) \equiv \text{Prob}(E_{\min} > \epsilon)}

for large M. To achieve this, we rely on three key relations:

• *First relation (exact)*:

$Q_{M}(\epsilon )=\left(P^{>}(\epsilon )\right)^{M}$

• *Second relation (estimate)*: The typical value of the minimum energy, $E_{\min }^{\text{typ}}$, satisfies:

$P^{<}(E_{\min }^{\text{typ}})={\frac {1}{M}}$

• *Third relation (approximation)*: For $M\to \infty$, we have:

$Q_{M}(\epsilon )=e^{M\log(1-P^{<}(\epsilon ))}\sim \exp \left(-MP^{<}(\epsilon )\right)$

Gaussian Case and Beyond

For a Gaussian distribution, the asymptotic tail of $P^{<}(E)$ as $E\to -\infty$ is:

$P^{<}(E)=\int _{-\infty }^{E}dx\,p(x)\sim {\frac {\sigma }{{\sqrt {2\pi }}|E|}}e^{-{\frac {E^{2}}{2\sigma ^{2}}}}$

Thus, the typical value of the minimum energy is:

$E_{\min }^{\text{typ}}=-\sigma {\sqrt {2\log M}}+{\frac {1}{2}}{\sqrt {\log(\log M)}}+O(1)$

Let us to be more general and consider tails

$P^{<}(E)\sim e^{-{\frac {|E|^{\gamma }}{2\sigma _{M}^{2}}}}\;.$

In the spirit of the central limit theorem we are looking for a scaling form $E_{\min }=a_{M}+b_{M}z$. The constants $a_{M},b_{M}$ are M-dependent while $z$ is a random variable of order one drawa from the M-independent distribution $P(z)$. Shows that

• at the leading order $a_{M}=-\left[2\sigma _{M}^{2}\log M\right]^{1/\gamma }$
• $b_{M}={\frac {2\sigma _{M}^{2}}{\gamma |a_{M}|^{\gamma -1}}}$
• $P(z)=e^{z}e^{-e^{z}}$ which is the Gumbel distribution

Ground State Fluctuations

Depending on the distribution $p(E)$, we observe different dependencies of M for $a_{M}$ and $b_{M}$. To emphasize the N dependence, we define:

$b_{M}\equiv 1/y_{N}\propto N^{\omega }$

Note that the typical fluctuations of the minimum are $\sim 1/y_{N}$. Specifically, we can write:

${\overline {\left(E_{\min }-{\overline {E_{\min }}}\right)^{2}}}\propto N^{2\omega }$

We will see that three distinct scenarios emerge depending on the sign of $\omega$.

Density of states above the minimum

For a given disorder realization, we compute $n(x)$, the number of configurations above the minimum with an energy smaller than $E_{\min }+x$. The key relation for this quantity is:

${\text{Prob}}[d(x)=k]=M{\binom {M-1}{k}}\int _{-\infty }^{\infty }dE\;p(E)[P^{>}(E)-P^{>}(E+x)]^{k}P^{>}(E+x)^{M-k-1}$

Taking the average, we get ${\overline {n(x)}}=\sum _{k}k\,{\text{Prob}}[d(x)=k]$. We use the following identity

$\sum _{k=0}^{M-1}k{\binom {M-1}{k}}(A-B)^{k}B^{M-1-k}=(A-B){\frac {d}{dA}}\sum _{k=0}^{M-1}{\binom {M-1}{k}}(A-B)^{k}B^{M-1-k}=(M-1)(A-B)A^{M-2}$

we arrive to the final form

${\overline {n(x)}}=M(M-1)\int dE\;p(E)\left[P^{>}(E)-P^{>}(E+x)\right]P^{>}(E)^{M-2}$

Replace $E=a_{M}+b_{M}z$ and obtain

${\overline {n(x)}}=\left(e^{y_{N}x}-1\right)\int _{-\infty }^{\infty }dze^{2z-e^{z}}=\left(e^{y_{N}x}-1\right)\quad {\text{with}}\;y_{N}\sim N^{-\omega }$

Density of States Above the Minimum

For a given disorder realization, we compute $n(x)$, the number of configurations above the minimum with energy smaller than $E_{\min }+x$. The key relation for this quantity is:

${\text{Prob}}[d(x)=k]=M{\binom {M-1}{k}}\int _{-\infty }^{\infty }dE\;p(E)\left[P^{>}(E)-P^{>}(E+x)\right]^{k}P^{>}(E+x)^{M-k-1}$

Taking the average, we get:

${\overline {n(x)}}=\sum _{k}k\,{\text{Prob}}[d(x)=k]$

We use the identity:

$\sum _{k=0}^{M-1}k{\binom {M-1}{k}}(A-B)^{k}B^{M-1-k}=(A-B){\frac {d}{dA}}\sum _{k=0}^{M-1}{\binom {M-1}{k}}(A-B)^{k}B^{M-1-k}=(M-1)(A-B)A^{M-2}$

This leads to the final form:

${\overline {n(x)}}=M(M-1)\int dE\;p(E)\left[P^{>}(E)-P^{>}(E+x)\right]P^{>}(E)^{M-2}$

Replacing $E=a_{M}+b_{M}z$, we obtain:

${\overline {n(x)}}=\left(e^{y_{N}x}-1\right)\int _{-\infty }^{\infty }dz\,e^{2z-e^{z}}=\left(e^{y_{N}x}-1\right)\quad {\text{with}}\;y_{N}\sim N^{-\omega }$

The Glass Phase

In the glass phase, the measure is concentrated in a few configurations with finite occupation probability, while in the paramagnetic phase, the occupation probability is $\sim 1/M$. As a result, the entropy is extensive in the paramagnetic phase but sub-extensive in the glass phase. It is useful to compare the weight of the ground state against the weight of other states. Define:

${\frac {\sum _{\alpha }z_{\alpha }}{z_{\alpha _{\min }}}}=1+\sum _{\alpha \neq \alpha _{\min }}z_{\alpha }\sim 1+\int _{0}^{\infty }dx\,e^{-\beta x}\left(e^{y_{N}x}-1\right)$

• In the high-temperature phase, for $\beta <y_{N}$, the occupation probability is close to zero, meaning that the ground state is not deep enough to make the system glassy.
• In the low-temperature phase, for $\beta >y_{N}$, the above integral is finite. Hence, setting $\beta =1/T$ and $T_{f}=1/y_{N}$, we find:

${\frac {z_{\alpha _{\min }}}{\sum _{\alpha =1}^{M}z_{\alpha }}}={\frac {1}{1+{\frac {T^{2}}{T_{f}-T}}}}$

This implies that below the freezing temperature $T_{f}$, the ground state is occupied with a finite probability, similar to Bose-Einstein condensation.

Take home message

Let us recall $y_{N}\sim N^{-\omega }$, so that three situations can occur

• For $\omega <0$, there is no freezing transition as there are too many states just above the minimum. This is the situation of many low-dimensional systems such as the Edwards Anderson model is two dimensions.
• For $\omega >0$ there are two important features: (i) there is only the glass phase, (ii) the system condensate only in the ground state because the excited states have very high energy. We will see that in real systems (i) is not always the case and that the exponent $\omega$ can change with temperature. This situation can be realistic (there is a very deep groud sate), but it is not revolutionary as the following one.
• For $\omega =0$ there is for sure a freezing transition. For the Random Energy Model defined above $T_{f}=1/{\sqrt {2\log 2}}$ One important feature of this transition that we will see in the next tutorial is that the condensation does not occur only in the ground state but in a large (yet not extensive) number of low energy exctitations.

References

• Spin glass i-vii, P.W. Anderson, Physics Today, 1988
• Spin glasses: Experimental signatures and salient outcome, E. Vincent and V. Dupuis, Frustrated Materials and Ferroic Glasses 31 (2018).
• Theory of spin glasses, S. F. Edwards and P. W. Anderson, J. Phys. F: Met. Phys. 5 965 (1975).
• Non-linear susceptibility in spin glasses and disordered systems, H. Bouchiat, Journal of Physics: Condensed Matter, 9, 1811 (1997).
• Solvable Model of a Spin-Glass, D. Sherrington and S. Kirkpatrick, Physical Review Letters, 35, 1792 (1975).
• Random-Energy Model: An Exactly Solvable Model of Disordered Systems, B.Derrida,Physical Review B, 24, 2613 (1980).
• Extreme value statistics of correlated random variables: a pedagogical review, S. N. Majumdar, A. Pal, and G. Schehr, Physics Reports 840, 1-32, (2020).