Mean field approximation and entropy

In summary, the conversation discusses a D-dimensional Ising model with N sites and the Hamiltonian that defines it. The entropy of the system is shown to be equal to N multiplied by the logarithm of 2, minus half of 1 plus the magnetization, multiplied by the logarithm of 1 plus the magnetization, minus half of 1 minus the magnetization, multiplied by the logarithm of 1 minus the magnetization. In the mean field approximation, the expression for the free energy at a given temperature and magnetization is given. The conversation also touches on the method of counting states and the relevance of spins being on a lattice.
  • #1
CAF123
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Homework Statement


Consider a D dimensional Ising model with N sites, defined by the Hamiltonian $$\mathcal H = -J \sum_{\langle i j \rangle} \sigma_i \sigma_j - h \sum_i \sigma_i$$ where the sum extends over nearest neighbours and each spin variable ##\sigma_i = \pm 1##. For a given spin configuration we denote the number of spins up and spins down by ##N_+## and ##N_-## respectively. The magnetisation is defined by ##m=(N_+ - N_-)/N##

a) Using Stirling's approximation, show that the entropy of the system can be written as $$S/N = \log 2 - \frac{1}{2} (1+m) \log(1+m) - \frac{1}{2} (1-m) \log(1-m)$$ and that in the mean field approximation $$f(T,m) = -\frac{1}{2} Jzm^2 + \frac{1}{2} T((1+m) \log(1+m) - (1-m) \log(1-m)) - T\log 2$$

Homework Equations


$$S = \ln \Omega,$$ in units of ##k_B=1##

The Attempt at a Solution


There are N sites and on each site, the spin can be up or down. So isn't the total number of spin configurations (or number of microstates) just ##2^N##? This would give ##S = N \log 2##, the first term in that expansion but I don't see how the other terms come about.

Thanks!
 
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  • #2
This doesn't seem like an introductory homework, but in any case I got the first part to work by using ## \Omega=N!/((N_+)!(N_-)!) ## . The number of states is apparently given by the number of possible combinations that can create a given ## m ##. It's an exercise in algebra, but their answer for S is correct.
 
  • #3
Hi Charles Link,
Charles Link said:
This doesn't seem like an introductory homework, but in any case I got the first part to work by using ## \Omega=N!/((N_+)!(N_-)!) ## . The number of states is apparently given by the number of possible combinations that can create a given ## m ##. It's an exercise in algebra, but their answer for S is correct.
Ah ok. I tried this expression for ##\Omega## and I did not seem to get the correct result - in particular where would the factor of log 2 come from?

Thanks!
 
  • #4
The factor of ## \ln 2 ## (it's actually ## N \ln 2 ## ) is what is left over after a lot of terms cancel. If I remember correctly ## N_+=N(1+m)/2 ## and ## N_-=N(1-m)/2 ## There are several places where you use ## N=N_+ + N_- ## and when you take ## \ln(\Omega) ## , Stirling's formula is used 3 times. (twice in the denominator). If you are careful with the algebra, I think you will get the same result. (I think the ## N \ln(2) ## comes from the 1/2's in the denominators of ## N_+ ## and ## N_- ##, but I'd need to look again at my calculations to be sure.)
 
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  • #5
I see. thanks! The only thing I don't understand is why would the number of states be counted in this way? If I just count the number of spin configurations then the total number of states would be ##2^N## if I can distinguish between the spins. Then this would yield an entropy with only the first term present. Any ideas?
 
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  • #6
I had Statistical and Thermal physics (we used the book by F. Reif), quite a number of years ago. If the spins are in a lattice, I think your way of counting them might be appropriate, but I am certainly no expert. I picked the method of the combinations simply because it then resulted in the book's answer.
 
  • #7
Charles Link said:
I had Statistical and Thermal physics (we used the book by F. Reif), quite a number of years ago. If the spins are in a lattice, I think your way of counting them might be appropriate, but I am certainly no expert. I picked the method of the combinations simply because it then resulted in the book's answer.
I see, thanks anyway! ok maybe we can await an answer from someone else, @TSny maybe? I thought my spins were on a lattice no? I imagined the Ising model in D dim to be like a hypercubic with spins placed on the nodes of the cubes.
 
  • #8
The spins are on a lattice.

2N represents the total number of possible micro states. You want the entropy corresponding to a particular value of magnetization m. Only a subset of the 2N possible micro states have the magnetization m. Charles gave you an expression for the number of micro states Ω in the subset. If you use his expression you should get the desired expression for S/N corresponding to a magnetization m.
 
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1. What is mean field approximation?

Mean field approximation is a mathematical technique used to simplify complex systems by assuming that each variable in the system is independent from one another. This allows for easier calculations and predictions of the behavior of the system.

2. How is mean field approximation related to entropy?

Mean field approximation is often used to calculate the entropy of a system. By assuming that each variable is independent, the total entropy of the system can be approximated by summing the entropy of each individual variable.

3. Can mean field approximation be applied to any system?

No, mean field approximation is only applicable to systems that exhibit a large number of interacting components. It is not suitable for systems with strong correlations or interactions between variables.

4. What are the limitations of mean field approximation?

Mean field approximation is a simplification technique and therefore, it may not accurately capture the behavior of a complex system. It also assumes that all variables are independent, which may not always be the case.

5. How is mean field approximation used in scientific research?

Mean field approximation is commonly used in fields such as physics, chemistry, and biology to model and predict the behavior of complex systems. It allows scientists to make approximations and simplify calculations, making it a valuable tool in research and analysis.

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