Potts Model in statistical physics

In summary, the conversation discusses the 3 state Potts model, which is defined by a Hamiltonian that encourages neighboring spins to have the same value. The spin variables can take values 1, 2, or 3, and the spins are on a d-dimensional hypercubic lattice. The conversation then considers an easier Hamiltonian and computes a quantity in relation to it. Part b) uses variational mean field theory to find the best lower bound for the original partition function. The resulting mean field equation is also provided. The conversation concludes with a question about the given formula in part a).
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CAF123
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Homework Statement


The 3 state Potts model is defined by $$-\beta \mathcal H = J \sum_{r,r'} (3 \delta_{\sigma(r), \sigma(r')} - 1) + h\sum_r \delta_{\sigma(r),1},$$ with J > 0 to encourage neighbouring Potts spins to have same value and h orienting field. The spin like variables can take values 1,2 or 3. The spins are on a d dim hypercubic lattice so that each spin has z=2d nearest neighbours.

Consider the easier hamiltonian $$-\beta \mathcal H_o = H \sum_r \delta_{\sigma(r),1}$$ where H is an external field.

a) Compute the quantity ##\langle -\beta(\mathcal H- \mathcal H_o)\rangle## wrt easy hamiltonian. Use the fact that, wrt the easy hamiltonian, $$\langle \delta_{\sigma, \sigma'} = \langle \delta_{\sigma,1}\rangle^2 + \langle \delta_{\sigma,2}\rangle^2 + \langle \delta_{\sigma,3}\rangle^2 = \langle \delta_{\sigma,1}\rangle^2 + \frac{(1-\langle \delta_{\sigma,1}\rangle)^2}{2}$$

b) Use variational mean field theory to find the best lower bound for the original partition function using the easy hamiltonian above. Show that the resulting mean field equation is $$m = \frac{e^{h+3Jzm} - 1}{e^{h+3Jzm} + 2}$$

Homework Equations


All in section 1, and ##\sum_{\sigma} e^{-\beta \mathcal H_o}## is partition function associated with system governed by easy hamiltonian

The Attempt at a Solution


I found the partition function associated with the easy hamiltonian and the object for part a) is: $$\langle (h-H) \sum_r \delta_{\sigma(r),1} + J \sum_{\langle r,r'\rangle} (3 \delta_{\sigma(r), \sigma(r')} - 1) \rangle_{0,H}$$ There are N nodes with a spin and at each node the average of ##\delta_{\sigma(r),1}## is (1+0+0)/3 = 1/3, so for N spins, the first term average is N/3. For the second term, I get a 2dN multiplied by average of ##\langle \delta_{\sigma(r),\sigma(r')}\rangle##. I can use the suggested formula and I get this to also evaluate to 1/3.

I am just wondering how this equation they gave comes about. I can write $$\langle \delta_{\sigma,\sigma'}\rangle = \sum_{\sigma, \sigma'} \delta_{\sigma, \sigma'}e^{-\beta \mathcal H_o}$$ which is written like $$\langle \delta_{1,\sigma'}\rangle \langle \delta_{\sigma,1}\rangle + \dots$$ I guess?

Thanks!
 
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Can anyone help me understand the formula in a)? part b) should be fine thereafter. Thanks
 

1. What is the Potts Model in statistical physics?

The Potts Model is a mathematical model used in statistical physics to study the behavior of a system with discrete, localized states. It is named after British mathematician Richard Potts and is often used to model phase transitions and critical phenomena in physical systems.

2. How does the Potts Model differ from the Ising Model?

Both the Potts Model and the Ising Model are used to study phase transitions in physical systems, but the Potts Model allows for more than two possible states at each location, whereas the Ising Model only allows for two states. This makes the Potts Model more suitable for studying systems with multiple components or interactions between particles.

3. What are the main applications of the Potts Model?

The Potts Model has been used in a wide range of applications, including the study of magnetism, lattice gases, percolation, and social dynamics. It has also been applied in computer science, image processing, and biology to model various phenomena such as neural networks and tumor growth.

4. How is the Potts Model solved?

The Potts Model can be solved using various techniques, including mean-field theory, Monte Carlo simulations, and renormalization group methods. Mean-field theory provides approximations for the behavior of the system, while Monte Carlo simulations use random sampling to approximate the behavior of the system. Renormalization group methods involve coarse-graining the system to analyze its behavior at different length scales.

5. What is the significance of the Potts Model in statistical physics?

The Potts Model has been a highly influential tool in statistical physics, providing insights into the behavior of complex physical systems and helping to uncover universal behavior at critical points. It has also been used as a starting point for more advanced models, such as the clock model and the Ashkin-Teller model, which have further contributed to our understanding of critical phenomena and phase transitions.

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