• kai_sikorski
In summary, the conversation discusses the use of MD simulations to study a Lennard-Jones fluid and its radial distribution function (RDF). The results show that the minimum of the RDF after the first maximum is still above 1, which is unexpected. The speaker is unsure if this is due to numerical issues or the model parameters chosen. Suggestions are given to look at the RDF for different temperatures and consider the effects of attraction and repulsion between particles.
kai_sikorski
Gold Member
I've been playing around with some MD simulations, a field not really familiar to me. I put together a code in LAMMPS to simulate a Lennard-Jones fluid and compute the RDF. I get the oscillations one would expect, which is good, but what is surprising is that the minimum of g(r) after the first maximum is still above 1 (where g(r) has been scaled such that a uniform distribution in space would give 1).

I haven't worked in this field too much previously so I'm not sure, but I guess I would have thought that the minimum should be below 1.

Opinions on whether this is more likely to be caused by some numerical issues or maybe the model parameters I choose?

I'm just looking for general suggestions on what could cause this, so I'm not posting the details of my LAMMPS script or my parameters. If people really want to see them I can.

Thanks!

Since you are looking for general suggestions:
- have you looked at and compared the RDF for different temperatures, particularly in the gas-like and in the liquid-like state and the transition states between them?
- Why would the minimum be below the value expected for non-interacting particles (assuming that is what you meant with your normalization)? You have an attraction, which should cause the value to be above 1. Also, you have the effect that other nearby particles (whose increased probability to exist are reflected by the first maximum) tend to repulse other particles, causing an anti-correlation. On this level of naivety, this is two competing effects, both of them purely qualitatively. Is there a reason why the 2nd effect should always be stronger than the first?

## 1. What is a radial distribution function?

A radial distribution function is a mathematical function that describes the probability of finding a particle at a certain distance from a reference particle in a system. It is commonly used in statistical mechanics to study the spatial arrangement of particles in a system.

## 2. How is a radial distribution function calculated?

A radial distribution function is calculated by dividing the number of particles found at a certain distance from the reference particle by the total number of particles in the system. This calculation is repeated for various distances to generate a plot of the radial distribution function.

## 3. What information can be obtained from a radial distribution function?

A radial distribution function can provide information about the structure and interactions of particles in a system. It can reveal patterns and correlations in the spatial arrangement of particles, such as the presence of clusters or voids.

## 4. What factors can affect the shape of a radial distribution function?

The shape of a radial distribution function can be influenced by various factors such as particle size, particle concentration, and temperature. Additionally, the type of intermolecular forces between particles and the shape of the particles themselves can also impact the shape of the radial distribution function.

## 5. How is a radial distribution function used in scientific research?

A radial distribution function is commonly used in molecular dynamics simulations and experiments to understand the structure and dynamics of particles in a system. It is also utilized in fields such as materials science, chemistry, and biophysics to study the properties of different systems and to design new materials with desired properties.

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