Normalization of Radial Distribution Function

In summary, the speaker is asking for suggestions on how to normalize the y-axis values in a Radial Distribution Function where the y-axis values are up to 40, while the other atoms' values are within 5. They have also attached an image and are open to a Python program for normalization. The usual normalization is for g(r) to equal 1 for large r, but the speaker is looking for a way to reduce the y-axis scale for better visualization of all the graphs.
  • #1
DHN
9
0
Hello all,
I have a Radial Distribution Function in which the y-axis ie., g(r) value goes up to 40. But the other atoms values for g(r) are, say within 5. So when i plot these two it is difficult to see the smaller graph.
So how do i normalize these value..??
I have attached an image.

Any python program if possible would be highly appreciated.

Thank you.
 

Attachments

  • RDF.png
    RDF.png
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Physics news on Phys.org
  • #2
The usual normalization is that g(r) ---> 1 for large r. For comparison with smaller peaks, you can truncate the y-axis.
 
  • #3
I want the y-axis scale to be reduced and hence i can have a better visualization w.r.t all the graphs.
Any suggestions on how to get this??
 

1. What is the purpose of normalizing the radial distribution function?

The radial distribution function measures the probability of finding a particle at a certain distance from a reference particle in a given system. Normalizing this function ensures that the total area under the curve is equal to 1, which allows for comparison between different systems and easier interpretation of the data.

2. How is the radial distribution function normalized?

The radial distribution function is normalized by dividing the value at each distance by the total number of particles in the system and then dividing by the volume of the system. This results in a probability distribution that ranges from 0 to 1.

3. Why is the normalization of the radial distribution function important in molecular simulations?

In molecular simulations, the radial distribution function is a useful tool for understanding the structure and interactions of particles in a system. Normalizing this function allows for easy comparison between different systems and helps to identify patterns and trends in the data.

4. Can the normalization of the radial distribution function be affected by system size or density?

Yes, the normalization of the radial distribution function can be affected by system size and density. As the number of particles or the volume of the system changes, the normalization factor will also change, which can affect the shape and values of the distribution function.

5. Are there any limitations to normalizing the radial distribution function?

One limitation of normalizing the radial distribution function is that it assumes a uniform distribution of particles in the system. This may not always be the case, especially in systems with high particle density or strong interactions between particles.

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