L-1: Difference between revisions
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In the above integral, <math> E </math> is the energy of the minimum. Hence, we can use the Taylor expansion <math> A(E) = a_M -y_N (E -a_M)</math>. In particular we can write | In the above integral, <math> E </math> is the energy of the minimum. Hence, we can use the Taylor expansion <math> A(E) = a_M -y_N (E -a_M)</math>. In particular we can write | ||
* <math> p(E) = \frac{d}{d E} P^<(E)= -A'(E) e^{-A(E)} \sim y_N e^{y_N (E -a_M)} /M</math> | * <math> p(E) = \frac{d}{d E} P^<(E)= -A'(E) e^{-A(E)} \sim y_N e^{y_N (E -a_M)} /M</math> | ||
* <math> P^>(E) - P^>(E+x) = e^{-A(E+x)}-e^{-A(E)}\sim e^{y_N (E -a_M)} \left(e^{ | * <math> P^>(E) - P^>(E+x) = e^{-A(E+x)}-e^{-A(E)}\sim e^{y_N (E -a_M)} \left(e^{y_N x}-1\right)/M </math> | ||
* <math> P^>(E)^{M-2}= Q_{M-2} (E) \sim \exp\left(-e^{ y_N (E-a_M)}\right) </math> | * <math> P^>(E)^{M-2}= Q_{M-2} (E) \sim \exp\left(-e^{ y_N (E-a_M)}\right) </math> | ||
Calling <math>u=y_N (E -a_M) </math> we obtain | Calling <math>u=y_N (E -a_M) </math> we obtain | ||
<center><math> \overline{d(x)} = \left(e^{ | <center><math> \overline{d(x)} = \left(e^{y_N x}-1\right) \int_{-\infty}^{\infty} du e^{2 u -e^{u} } = \left(e^{y_N x}-1\right) | ||
</math></center> | </math></center> | ||
Revision as of 22:54, 28 November 2023
Spin glass Transition
Experiments
Parlare dei campioni di rame dopati con il magnesio, marino o no: trovare due figure una di suscettivita e una di calore specifico, prova della transizione termodinamica.
Edwards Anderson model
We consider for simplicity the Ising version of this model.
Ising spins takes two values 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=\pm 1} and live on a lattice of 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 N } sitees 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 i=1,2,\ldots,N } . The enregy is writteen as a sum between the nearest neighbours <i,j>:
Edwards and Anderson proposed to study this model for 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 J } that are i.i.d. random variables with zero mean. We set 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 \pi(J)} the coupling distribution indicate the avergage over the couplings called disorder average, with an overline:
It is crucial to assume 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 \bar{ J}=0 } , otherwise the model displays ferro/antiferro order. We sill discuss two distributions:
- 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 \pi(J) =\exp\left(-J^2/2\right)/\sqrt{2 \pi}}
- Coin toss 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 J= \pm 1 } , selected with probability 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 1/2 } .
Edwards Anderson order parameter
The SK model
Sherrington and Kirkpatrik considered the fully connected version of the model with Gaussian couplings:
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
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 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 \beta_c=?? } .
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
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^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,...,E_M} as iid variables, drawn from the 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, but we remain general in this section). It is useful to use the following notations:
- 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) } , it is the probability to find 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_E^{+\infty} dx p(x) = 1- P^<(E) } , it is the probability to find an energy larger than E.
We denote
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 need two key relations:
- The first relation is exact:
- The second relation identifies the typical value of the minimum, namely 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_M } :
.
Let us consider the limit, 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 \lim_{M\to \infty} (1-\frac{k}{M})^M =\exp(-k)} , which allow to re-write the first relation:
This relation holds only when 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 \epsilon \approx a_M } and one hase to expand around this value. However, a direct Taylor expansion does not ensures that probabilities remain positive. Hence, 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 P^<(\epsilon)=\exp(-A(\epsilon)) } and remark 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(\epsilon) } is a decreasing function. We propose the following Taylor expansion
Depending on the 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)} we have a different dependence on N or M of both 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_M, y_N } . It is convenient to define
We will see that three different scenarios occur depending on the sign of 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 \omega } . Using this expansion we derive:
- The famous Gumbel distribution:
- the typical fluctuations of the minimum 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 \sim 1/y_N} . In particular we can write:
Density of states above the minimum
For a given disorder realization, we compute 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 d(x) } , the number of configurations above the minimum with an energy smaller than 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}+x} . The key relation for this quantity is:
Taking the average 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 \overline{d(x)} = \sum_k k \text{Prob}(d(x) = k) } , we derive
In the above integral, is the energy of the minimum. Hence, we can use the Taylor expansion . In particular we can write
Calling we obtain
Exercise L1-A: the Gaussian case
Specify these results to the Guassian case and find for
- the typical value of the minimum
%
- The expression
- The expression of the Gumbel distribution for the Gaussian case
Bibliography
- Theory of spin glasses, S. F. Edwards and P. W. Anderson, J. Phys. F: Met. Phys. 5 965, 1975