Mean field models

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A mean field model, or a mean field solution of a model, is an approximation to the actual solution of a model in statistical physics. The model is made exactly solvable by treating the effect of all other particles on a given one as a mean field (hence its name). It appear in different forms and different contexts, but all mean field models have this feature in common.

Mean field solution of the Ising model

A well-known mean field solution of the Ising model, known as the Bragg-Williams approximation goes as follows. From the original Hamiltonian,

 U = - J \sum_i^N S_i \sum_{<j>} S_j ,

suppose we may approximate

 \sum_{<j>} S_j \approx n \bar{s},

where n is the number of neighbors of site i (e.g. 4 in a 2-D square lattice), and \bar{s} is the (unknown) magnetization:

 \bar{s}=\frac{1}{N} \sum_i S_i .

Therefore, the Hamiltonian turns to

 U = - J n \sum_i^N S_i \bar{s} ,

as in the regular Langevin theory of magnetism: the spins are independent, but coupled to a constant field of strength

H= J n \bar{s}.

The magnetization of the Langevin theory is

  \bar{s} = \tanh( H/k_B T ).

Therefore:

  \bar{s} = \tanh(J n\bar{s}/k_B T).

This is a self-consistent expression for \bar{s}. There exists a critical temperature, defined by

k_B T_c= J n .

At temperatures higher than this value the only solution is \bar{s}=0. Below it, however, this solution becomes unstable (it corresponds to a maximum in energy), whereas two others are stable. Slightly below T_c,

\bar{s} =\pm\sqrt{3\left(1 - \frac{T}{T_c}\right)}.