Ising model autocorrelation function calculation

In summary, the speaker is trying to measure the autocorrelation functions of a 2D Ising model using a specific equation. They have calculated a c(t) value of magnetization, but the results are not satisfactory. They are seeking help in identifying any errors in their calculation.
  • #1
yklee
1
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Homework Statement
on going
Relevant Equations
$$c(t) = \langle A(t'+t) A(t) \rangle - \langle A \rangle^{2} = \frac{1}{t_{max}-t}\sum_{t'=0}^{t_{max}-t} A(t'+t)A(t) - \frac{1}{t_{max}-t}\sum_{t'=0}^{t_{max}-t}A(t'+t) \cdot \frac{1}{t_{max}-t} \sum_{t'=0}^{t_{max}-t}A(t)$$
Dear Mr. and Ms.,

I am trying to measure the autocorrelation functions of 2D ising model based on the equation given by
1659496169335.png

where A(t) denote a measure. I calculate a c(t) of magnetization. I calculated in this way

acf:
    data_path = f"../../trajectory/data.txt"
    data = np.loadtxt(data_path)
    Nsteps =  len(data)
    acf = np.zeros(Nsteps, float)
    acf[0] = 1.000
    for t in range(1,Nsteps):
        value = np.mean(data[t:Nsteps]*data[:Nsteps-t]) - np.mean(data[t:Nsteps])*np.mean(data[:Nsteps-t])
        act[t] = value

However, the results seems not good. Can you discuss the wrong part of my calculation?
 
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  • #2
What is the act[] array? Everything else is acf. I am guessing this is a typo when you entered it here, as this should throw an error.

Other than that - a quick look at your code appears like it should replicate your formula.

Try running this on a small set of data, first - something that you could verify the result of. Then you can scale up to the full data.
 

FAQ: Ising model autocorrelation function calculation

What is the Ising model?

The Ising model is a mathematical model used to study the behavior of a collection of interacting particles, such as atoms or spins in a magnetic material. It was first proposed by physicist Ernst Ising in 1925 and has since been used in various fields of physics, chemistry, and materials science.

How is the autocorrelation function calculated in the Ising model?

The autocorrelation function in the Ising model is calculated by measuring the correlation between the spins of particles at different points in time. This is done by taking the average of the product of the spin values at two different time steps. The result is a measure of how strongly the spin values at different times are correlated.

What is the significance of the autocorrelation function in the Ising model?

The autocorrelation function in the Ising model is used to study the dynamics and behavior of the system. It can provide information about the time scales of relaxation and the critical behavior of the system, such as phase transitions.

How does temperature affect the autocorrelation function in the Ising model?

The temperature of the system can greatly affect the autocorrelation function in the Ising model. At low temperatures, the system tends to stay in a more ordered state, resulting in a slower decay of the autocorrelation function. At higher temperatures, the system is more disordered and the autocorrelation function decays more quickly.

Are there any limitations to using the Ising model autocorrelation function?

Like any mathematical model, the Ising model has its limitations. It assumes that all particles are in thermal equilibrium and that interactions between particles are only between nearest neighbors. These assumptions may not hold true in all real-world systems, so the results from the Ising model should be interpreted with caution.

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