Learning 1D Ising Model With Nearest Site Interaction

In summary,The 2-D ising model is significantly more complicated than the 1-D. There's a brief treatment in Landau and Lifgarbagez, vol. 5 (pp 498-506) and Chaikin and Lubensky "Principles of condensed matter physics" has additional detail along with the O_n model.
  • #1
KFC
488
4
I am learning the 1D ising model (spin 1/2), without external field and considering the nearest site interaction, the hamiltonian for 1D chain is simple

[tex]H = -J\sum_i S_iS_{i+1}[/tex]

Since each spin can only take either +1 or -1, we can write the transition matrix as

[tex]
T =
\left(
\begin{matrix}
e^{K} & e^{-K} \\
e^{-K} & e^{K}
\end{matrix}
\right)
[/tex]
where [tex]K=\beta J[/tex]

Now I try to learn 2D case, I read some book on it but seems quite complicated, so I started with the simplest case (no external field, only nearest interaction, rectangular lattics with only 2 rows and N columns and perodic boundary condition). The hamiltonian is

[tex]H = -J\sum_{i=1}^{2}\sum_{j=1}^N S_{ij}S_{i, j+1}[/tex]

right?

What I am really consfuing is how to find the transition matrix? Now each site has four nearest neighbor (of course, to avoid double counting, we only need to count two one at a time, let's say we count the one next to and below the current site) and each spin can take 2 values, so what's the dimension of the transition matrix? and what does [tex]T_{ij}[/tex] means?
 
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  • #2
Here is my idea. Since we have to sum over all possible sites, so consider the site (1, 1), and (2, 1) (i.e. the current site and the site below it), the energy is

[tex]-J\left[\left(S_{11}S_{12}+S_{11}S_{21}\right) + \left(S_{21}S_{22} + S_{21}S{11}\right)\right][/tex]

All other sites has the same case as this specific site (1, 1), and in this case, we only need to consider four spin [tex]S_{11}, S_{12}, S_{21}, S_{22}[/tex]. Note that each of them can take two values so there are totally 16 possible values. The transition matrix would be 4x4, right?
 
  • #3
Your Hamiltonian is incorrect- it's missing a term like S_ij S_i+1,j .The 2-D ising model is significantly more complicated than the 1-D. There's a brief treatment in Landau and Lifgarbagez, vol. 5 (pp 498-506) and Chaikin and Lubensky "Principles of condensed matter physics" has additional detail along with the O_n model.
 
  • #4
Andy Resnick said:
Your Hamiltonian is incorrect- it's missing a term like S_ij S_i+1,j .The 2-D ising model is significantly more complicated than the 1-D. There's a brief treatment in Landau and Lifgarbagez, vol. 5 (pp 498-506) and Chaikin and Lubensky "Principles of condensed matter physics" has additional detail along with the O_n model.

Thanks. I will check
 
  • #5
Andy Resnick said:
Your Hamiltonian is incorrect- it's missing a term like S_ij S_i+1,j .The 2-D ising model is significantly more complicated than the 1-D. There's a brief treatment in Landau and Lifgarbagez, vol. 5 (pp 498-506) and Chaikin and Lubensky "Principles of condensed matter physics" has additional detail along with the O_n model.

I checked it, now the Hamiltonian is modified as

[tex]H = -J\sum_{i=1}^{2}\sum_{j=1}^N S_{ij}S_{i, j+1} + S_{ij}S_{i+1,j}[/tex]

So, the for site (i, j), the energy could take 16 possible values. For

[tex]S_{ij}, S_{i+1, j}, S_{i, j+1}, S_{i+1, j+1} \Longrightarrow E[/tex]

++++: 4
+++-: 2
++-+: 2
++--: 0
+-++: 2
+-+-: 0
+--+:-2
+---: -2
-+++: 2
-+-+: 0
-+--: -2
--++: 0
--+-: -2
---+: -2
----: 4

So could I just use this values to setup the transition matrix?
 
  • #6
The exact solution of the 2D Ising model was done by Onsager. See here for a brief outline: http://www.nyu.edu/classes/tuckerman/stat.mech/lectures/lecture_26/node2.html
 
  • #7
genneth said:
The exact solution of the 2D Ising model was done by Onsager. See here for a brief outline: http://www.nyu.edu/classes/tuckerman/stat.mech/lectures/lecture_26/node2.html

Thanks. I read that before, but I found this quite confusing.

The above outline is about n x n lattices, now let's apply his result to 2 x n, we get the matrix element

[tex]
T_{jk} = \exp\left[\beta J\left(\sigma_{1j}\sigma_{1k} + \sigma_{1j}\sigma_{2j} + \sigma_{2j}\sigma_{2k} + \sigma_{2j}\sigma_{1j}\right)\right]
[/tex]

For the last term, we apply the periodic boundary condition.

My doubt is [tex]\sigma_{ij}[/tex] is the eigenvalue and could be -1 or +1, so each terms in above expression couble be either +1 or -1, how come do we get a certain value for specific matrix element [tex]T_{jk}[/tex] ?
 
  • #8
Solvng the 2D Ising model is almost trivial by transforming it to a close packed dimer model. The dimer model can be solved using very simple combinatorial techiques, you don't need complicated transfer matrix techniques. See e.g. here:

http://arxiv.org/abs/cond-mat/0212363
 
  • #9
Count Iblis said:
Solvng the 2D Ising model is almost trivial by transforming it to a close packed dimer model. The dimer model can be solved using very simple combinatorial techiques, you don't need complicated transfer matrix techniques. See e.g. here:

http://arxiv.org/abs/cond-mat/0212363

Thank you very much. I will read that later.

I am learning this problem because there is one chapter about transfer matrix in my text and this method is useful in some other place. So I want to learn it by studying 2D ising model as an example.
 
  • #10
KFC said:
Thank you very much. I will read that later.

I am learning this problem because there is one chapter about transfer matrix in my text and this method is useful in some other place. So I want to learn it by studying 2D ising model as an example.



Then you should read this book:

http://tpsrv.anu.edu.au/Members/baxter/book

You can download it free of charge. The transfer matrix technique that most textbooks explain for solving the Ising model is of no use for most other models. The Ising model is a so-called "free fermion model", the transfer matrix can then be diagonalized using a Bogoliubov transformation. This won't work for the vast class of integrable models.

So, if you want to learn about solving models, you should learn about the Bethe Ansatz, the Yang-Baxter equation etc. etc. This is explained in detail in the book by Baxter.
 
  • #11
Using the 2D Ising model to learn about the Transfer matrix is probably not the best approach. First, the Transfer Matrices are somewhat obscure (although certainly doable), and second, it doesn't even get you half way there in solving the model (i.e. you still need perform some other steps as well - steps that are quite specific to the Ising model).

But just to throw in another book, I know that this one solves the Ising model using Transfer matrices:
https://www.amazon.com/dp/9810216424/?tag=pfamazon01-20
(Chapter 5.1)
 
  • #12
ok. Thank you for all your help.
 
  • #13
Is the Onsager Solution extendable to 3D ? is it do-able in principle ?
 

1. What is a 1D Ising model with nearest site interaction?

A 1D Ising model with nearest site interaction is a simplified mathematical model used to study the behavior of particles in a one-dimensional lattice structure. It assumes that the particles can only interact with their nearest neighbors and can only take on two possible states (typically represented as +1 and -1). This model is commonly used in statistical mechanics to understand phase transitions and critical phenomena.

2. How is the Ising model used for learning?

The Ising model can be used for learning by applying machine learning techniques to analyze the behavior of the particles in the lattice. By observing how the particles interact and change states, we can train models to predict future behavior and gain insights into the underlying dynamics of the system.

3. What is the significance of nearest site interaction in the Ising model?

The nearest site interaction assumption in the Ising model simplifies the calculation and analysis of the system, making it easier to study and understand. It also allows for the application of powerful mathematical techniques, such as the transfer matrix method, to analyze the system.

4. What are the real-world applications of learning with the Ising model?

The Ising model has been used in various fields, including physics, chemistry, biology, and computer science, to study a wide range of systems. Some examples include understanding the behavior of magnetic materials, predicting protein folding patterns, and analyzing social networks.

5. Are there any limitations to using the Ising model for learning?

While the Ising model has proven to be a valuable tool for studying complex systems, it also has its limitations. It assumes a simplified system with only two possible states and nearest neighbor interactions, which may not accurately reflect real-world situations. Additionally, the model may not be suitable for systems with long-range interactions or multiple phases.

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