Tuesday, May 28, 2013

Calculating Diffusion Coefficients from Random Walk Monte Carlo

This is taken from a lecture I gave during the Molecular Statistics course at University of Copenhagen, which I co-teach with +Jan Jensen  and +Jimmy Kromann. The students have to program different types of molecular simulations and are not expected to have any programming experience before the course.

Random walk Monte Carlo seems a bit silly at first glance, since a simulation does not really contain a lot of information. As the name implies, the motion of the particle is completely random, much like Brownian motion of a particle suspended in a fluid. The behavior of a particle in a random walk simulation is very much like the movement of the average particle in a fluid, which randomly and constantly bounces into other particles, every time from a random direction.
A collection on particles will then have average properties much like a liquid or (as we have looked a lot at in the course) the Lennar-Jones fluid. Do note, that  there are no interactions between any particles.

A simple one-particle random walk Monte Carlo simulation is shown here.

The equations of motion are shown here:
$$x(t + dt) =  x(t) \pm \mathrm{random.random()} \cdot \mathrm{d}l $$
$$y(t + dt) =  y(t) \pm \mathrm{random.random()} \cdot \mathrm{d}l $$

where random.random() is the Python default random number generator and $\mathrm{d}l$ is a scaling factor that determines the average step-length. The fact that the equation does not include any interaction terms makes it computationally cheap to simulate a lot of particles.

In the last Molecular Statistics class we used random walk Monte Carlo to obtain the diffusion coefficient via Fick's second law, here in one dimension:
$$\frac{\partial \phi}{\partial t} = D\ \frac{\partial^2 \phi}{\partial x^2}$$
 The solution of Fick's second law is a Gaussian function
$$\phi (x, t) = \frac{1}{\sqrt{2\ \pi\ \sigma^2}} \exp \left( - \frac{x^2}{2 \sigma^2}\right)$$
with $\sigma = \sqrt{2\ D\ t}$. So if we can calculate $\sigma$ from the distribution of particles, we can immediately get the diffusion coefficient.
The simple approach is to start a random walk simulation with many particles and then fit the distribution of particles to a Gaussian.

We used this extremely simple Python/Numpy program to simulate random walk and calculate $D$:

X = numpy.zeros(n_particles)

for i in range(n_steps):
    X += numpy.random.uniform(-dl, dl, n_particles)
    sigma = X.std()

    print "D =", sigma**2 / ( 2 * i)

The simulation looks like this in 2D (with 400 particles):

As you can see, the particles collectively look like they are diffusing through a medium. The diffusion constant is the derived (here only in the $X$-direction by fitting the distribution to a Gaussian function (which was simpy done via Numpy's .std() function). The time evolution of the distribution of particles (blue histogram) and the Gaussian fit (red curve) looks like this:

The time evolution of $D$ should converge pretty quickly. You can see $D$ for a simulation with 10,000 particles below. It took a matter of seconds on Jimmy's old laptop to run this. Imagine doing an long enough MD with 10,000 particles on your laptop!

Tuesday, May 7, 2013

Visualizing Pointer Aliasing in Python

This is a concept that has annoyed many a newcomer to Python - including the author of this post. I will be talking briefly about this concept in my lecture next Thursday, and the excellent TA, +Jimmy Charnley Kromann, pointed me to an example he saw on pythontutor. The Visualize tool on pythontutor is a very explicit way to illustrate what happens under the hood of python: http://www.pythontutor.com/visualize.html

The Visualize tool on pythontutor is a very clever way to illustrate what happens under the hood of python: http://www.pythontutor.com/visualize.html

EDIT: There seems to be a problem with python tutor at the moment. Click this link, if the embedded code below fails to load:  Python Example

EDIT: embedded code not working - click link:


If you want to avoid pointer aliasing,  you usually want to do one of these three things in order to copy a to b:


b = a[:] 

This takes a shallow copy. Personally, I don't like this option, because the slicing notation is not clear to everyone. But it is definitely the shorter notation. This will fail if you want to copy a list of lists. Being a shallow copy, it does not copy lists of lists. It will merely  copy the list of pointers to other "sub"-lists.


import copy
b = copy.copy(a)

This takes a shallow copy. It is more clear from the code what this does, so this is usually my preferred approach. Still a shallow copy, however.

import copy 
b = copy.deepcopy(a)

This is the fail-safe method and takes a copy of everything, so use this if you're in doubt. Works pretty much in every case. The down side is that it's usually slower to copy everything with deepcopy.

Figure 1 : a and b contain to the  
word "banana" in two different ways.