Two-body correlation function computation

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SUMMARY

The discussion focuses on computing the two-body correlation function (CF) in molecular dynamics (MD) to analyze excess entropy. The correlation function is defined as C(t, r, t', r') = ⟨X(t, r)Y(t', r')⟩, where averaging can be performed either by time or ensemble, depending on the property being studied. It is established that for relative distances, the CF transforms into the radial distribution function, necessitating the calculation of distances from one particle to others, followed by histogramming and normalization. The ultimate goal is to derive the free Gibbs energy through entropy or configuration integral calculations.

PREREQUISITES
  • Understanding of molecular dynamics (MD) simulations
  • Familiarity with correlation functions in statistical mechanics
  • Knowledge of radial distribution functions (RDF)
  • Experience with histogramming techniques for data analysis
NEXT STEPS
  • Study the mathematical formulation of two-body correlation functions in MD
  • Learn about the computation of radial distribution functions (RDF)
  • Explore methods for calculating free Gibbs energy from molecular simulations
  • Investigate time-averaging versus ensemble-averaging in MD simulations
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Molecular dynamics researchers, physicists studying statistical mechanics, and computational chemists focused on thermodynamic properties of atomic systems will benefit from this discussion.

ab_kein
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TL;DR
How to compute correlation function ##g(\vec r, \Omega)## from MD data?
I'm studying how to compute excess entropy in molecular dynamics (MD). I've found it is needed to compute the two-body correlation function (neglecting high-order terms), the details can be found, for example, in this article.

So the definition of correlation function (CF for short) is
##C(t, r,t',r')=\langle X(t,r)Y(t',r')\rangle##
where angle brackets mean averaging.

First question: is the averaging performed by time or ensemble (by all the atoms in the system)?

Second: for computing the CF, do I need to compute it in the stationary process? I mean, do I need to simulate a steady-state system in MD (probably to perform time-averaging) or can CF be found from one time point (using atom coordinates and velocities in a specific time moment)?

Third: If, for example, I want to compute CF for relative distance ##\vec r=\vec r_2 - \vec r_1##, where ##\vec r_1, \vec r_2## are the absolute positions of two atoms, what will be ##X## and ##Y##?I'm sorry if I've written something unclear, I'm always ready to clarify the question, and I'd be happy for any help.

P.S. My goal is to calculate the free Gibbs energy of bunch of atoms via computing entropy or configuration integral (partition function).
 
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It seems like I have understood the correlation function idea.

The averaging type depends on property we have to study, so for relative distance averaging is performed by all particles. And in case of correlation for relative distance CF is turned into radial distribution function, so to calculate it we have to take one particle, calculate distances to other particles, make a histogram with given $\Delta r$, repeat these steps for all particles, calculate the average bin heights and normalize obtained distribution. The same is for angular position CF $$g(\phi_{pos}, \theta_{pos})$$.
 

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