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Calculating Microcanonical Entropy in Molecular Dynamics

Physics Asked by user35952 on May 20, 2021

As a beginning, I am simulating Argon liquid at 94 K and characterising as it is done by the Rahman’s first paper on Molecular Dynamics. After going through the first two chapters of Art of Molecular Dynamics by D. C. Rapaport, I got interested in calculating the entropy of the system at hand (using a technique outlined in that book). In his book, he has used the fact that H-function can be written as (apart from a constant factor)
$$
H = int f(textbf v,t) log f(textbf v,t) dtextbf v
$$

where $f(textbf v,t)$ is the velocity distribution of the system at time $t$. Now as the simulation progresses, one should see that this $H$ function should increase with time (negative of entropy) as the system gets closer to equilibration and becomes a constant after it attains equilibration.

The main catch here is that, with a system that is not at the required temperature one has to scale the velocities for some time to achieve it. So because of this, I am not able to characterise or see this effective shift in H when I calculate and plot it. As it is seen in the image, the H-function drops to a low value and then raises again to reach a constant value.

My question is :

  • What is the best way to calculate entropy in Microcanonical ensemble ? If one avoids scaling, can we see the entropy of the system changing properly ?H-Function for the beginning time

One Answer

This method requires that you first calculate the distribution function $f(mathbf{v},t)$, which could be done by running a few simulations for a long time and binning the velocities. Once you have this distribution, you could use the Gibbs entropy expression

$$ <S>=-k_B<log(f)> $$

which is of course a time/ensemble average. During a simulation, at some instantaneous time, you should be able to evaluate $f(mathbf{v},t)$ from your previously calculated distribution. This gives you an instantaneous

$$ S=-k_Blog(f) $$

which can be averaged over time, and should converge to some value in an equilibrium simulation.

Answered by Drew on May 20, 2021

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