2D Ising Model (analytical expressions)

In summary, to check the results of a simulation of the 2D Ising Model under the Metropolis algorithm, the analytical expressions for the mean energy, magnetization, specific heat and magnetic susceptibility can be used. However, the expression for the mean energy may not give accurate results and there is currently no known analytical expression for the magnetic susceptibility.
  • #1
Orion_PKFD
9
0
Hi all,

I am doing a program to simulate the 2D Ising Model under the metropolis algorithm. In order to check my results I would like to compare them with the analytical expressions for the mean energy, magnetization, specific heat and magnetic susceptibility.

I already found the expressions for the mean energy, magnetization and specific heat. However, when I plot the expression for the mean energy the result does not look right. I used the one in Huang's book. Concerning the magnetic susceptibility, I couldn't find any analytical expression...

Anyone could give me some help? Thanks.

Best regards!
 
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  • #2
</code>The analytical expression for the mean energy of the 2D Ising Model is given by:E = -J * N * M^2, where N is the number of spins in the lattice and M is the average magnetization. This can be derived from the Hamiltonian of the Ising Model, which is given by: H = - J * sum(s_i * s_j), where J is the coupling constant and s_i and s_j are the spin values of two neighbouring sites.The analytical expression for the magnetization of the 2D Ising Model is given by:M = (1/N) * sum(s_i) where N is the number of spins in the lattice and s_i is the spin value of a particular site. This can be derived from the Hamiltonian of the Ising Model, which is given by: H = - J * sum(s_i * s_j), where J is the coupling constant and s_i and s_j are the spin values of two neighbouring sites.The analytical expression for the specific heat of the 2D Ising Model is given by:C = (k_B / T^2) * var(E) where k_B is Boltzmann's constant, T is the temperature, and E is the mean energy. This can be derived from the Hamiltonian of the Ising Model, which is given by: H = - J * sum(s_i * s_j), where J is the coupling constant and s_i and s_j are the spin values of two neighbouring sites.Finally, the analytical expression for the magnetic susceptibility of the 2D Ising Model is given by:X = (1/T) * var(M) where T is the temperature and M is the average magnetization. This can be derived from the Hamiltonian of the Ising Model, which is given by: H = - J * sum(s_i * s_j), where J is the coupling constant and s_i and s_j are the spin values of two neighbouring sites.
 

What is the 2D Ising Model?

The 2D Ising Model is a mathematical model that describes the behavior of a two-dimensional lattice of interacting spins. It is often used in statistical mechanics to study the properties of magnetic materials.

What are the analytical expressions used in the 2D Ising Model?

The most commonly used analytical expressions in the 2D Ising Model are the partition function, the free energy, and the correlation function. These expressions allow for the calculation of thermodynamic quantities such as magnetization, specific heat, and susceptibility.

What are the assumptions made in the 2D Ising Model?

The 2D Ising Model assumes that each spin can only take on two possible values (up or down), and that the interactions between spins are limited to neighboring spins on a lattice. It also assumes that the system is in thermal equilibrium and follows the laws of statistical mechanics.

How is the 2D Ising Model solved analytically?

The 2D Ising Model can be solved analytically using various techniques, such as mean field theory, transfer matrix method, and exact solutions for specific cases (e.g. the Onsager solution for a square lattice). These techniques involve simplifying assumptions and mathematical manipulations to obtain analytical expressions for thermodynamic quantities.

What are the applications of the 2D Ising Model?

The 2D Ising Model has been used to study a wide range of physical systems, including ferromagnets, superconductors, and even social and economic systems. It has also been extended to higher dimensions and more complex interactions, making it a versatile tool in statistical mechanics and other fields of study.

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