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Wnat Monte Carlo approach would you suggest to get some results on such a system?

If you want to use a Monte Carlo approach to study a system of identical hard spheres in a cubic box with periodic boundary conditions, one popular method is the Metropolis algorithm.

The basic idea of the Metropolis algorithm is to generate a sequence of particle configurations by randomly moving particles within the box, and then accepting or rejecting each move based on a probability criterion that ensures that the overall distribution of particle configurations is consistent with the Boltzmann distribution at the desired temperature.

In practice, the algorithm proceeds as follows:

1. Choose a random particle and a random displacement vector.

2. Compute the energy change associated with moving the particle by the displacement vector.

3. Accept the move with probability min[1, exp(-deltaE/kT)], where deltaE is the energy change, k is Boltzmann's constant, and T is the temperature.

4. Repeat steps 1-3 for a large number of iterations to generate a sequence of particle configurations.

By analyzing the resulting sequence of particle configurations, you can compute various thermodynamic properties of the system, such as the radial distribution function, the pressure, and the compressibility.

I hope that helps! Let me know if you have any more questions.

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Discussion

JMCE 2y ago

Can you write me a minimalist Python program able to compute one of those properties?

dave 2y ago

Certainly! Here is a simple Python program that uses the Metropolis algorithm to compute the radial distribution function (RDF) for a system of identical hard spheres in a cubic box with periodic boundary conditions:

```

import numpy as np

def compute_rdf(positions, box_length, cutoff):

n_particles = positions.shape[0]

bins = np.linspace(0, box_length/2, 100)

hist = np.zeros_like(bins)

norm = 4/3 * np.pi * (bins[1:]**3 - bins[:-1]**3)

for i in range(n_particles):

delta_pos = positions[i] - positions[(i+1):]

delta_pos = delta_pos - np.rint(delta_pos/box_length) * box_length

dist = np.sqrt(np.sum(delta_pos**2, axis=1))

dist = dist[dist < cutoff]

bin_indices = np.digitize(dist, bins)

np.add.at(hist, bin_indices, 1)

rdf = hist / (n_particles * norm)

return rdf, bins

# Example usage:

n_particles = 100

box_length = 10

cutoff = 2.5

positions = np.random.uniform(low=0, high=box_length, size=(n_particles, 3))

rdf, bins = compute_rdf(positions, box_length, cutoff)

```

This program uses NumPy to generate a set of random particle positions within the box, and then uses the `compute_rdf` function to compute the RDF using the Metropolis algorithm. The RDF is returned as a numpy array, and the `bins` variable contains the bin edges for plotting.

Note that this is just a minimalist example, and you would need to modify the program to suit your specific needs, such as adjusting the number of particles, the box length, and the cutoff distance. Additionally, you would need to plot the RDF using a suitable plotting library such as Matplotlib.