Wednesday, 27 April 2011

An early look at simulation

While I was putting together the post on 2D disks I came across a lovely paper from 1962 on 2D melting by Alder and Wainwright. From there I found this paper from 1959: Studies in Molecular Dynamics. I. General Method by the same authors.

They describe the "event driven" molecular dynamics (MD) algorithm. Normally, with MD you calculate forces, and thus accelerations, and update this way. Hard disks or spheres behave more like snooker balls, the forces are more or less instantaneous impulses that conserve momentum so it's better to deal with collision events and leave out the acceleration part.

The paper gives a fascinating insight into the early days of computer simulation (they still refer to them as "automatic computers"), what their limitations were and what details were worth worrying about. To give you an idea, in 1959 they say:
With the best presently available computers, it has been possible to treat up to five hundred molecules. With five hundred molecules it requires about a half-hour to achieve an average of one collision per molecule.
So in their case it was CPU speed that was the problem, they get about thousand collisions per hour. To put that in perspective, a modern event-driven simulation of a similar system will maybe hit about a billion collisions per hour on a reasonable desktop [source]. I don't say this to mock their efforts, these are the giants on whom's shoulders we stand. I'll come back to why that number is so comparatively big these days, first I want to look at visualisation.


In 1959 there were no jpegs or postscripts and certainly no Povray or VMD, I'm not sure they even had printers. So how do you visualise your simulation? Well they had a rather elegant answer to that. They could output the current state of the system to a cathode-ray tube as a bunch of dots in the positions of the particles. Then they pointed a camera at the screen and left the shutter open while they ran a simulation. What you get is these beautiful images below showing the particle trajectories. Firstly in a crystal phase you can see the particles rattling around their lattice sites

This is a projection of the FCC lattice (the squares confused me at first). In the fluid phase they do a little bit of cage rattling and then start to wander off.

[Figures reprinted with permission Alder and Wainwright, J. Chem. Phys. 31, 459 (1959). Copyright 1959, American Institute of Physics].

I honestly couldn't show it better today. Some people dismiss visualisations as pretty pictures that only exist to attract attention. Perhaps this is sometimes true but it only takes one look at this to see how they can stir the imagination and shape the intuition – and that's what creates new ideas.


I'd quickly like to come back to the speed difference between 1959 and today. A lot of the difference can be put down to Moore's law. After an annoying amount of Googling I can't really say how much faster modern CPUs are. A lot probably. However, I'd like to focus on an often overlooked factor – the development of algorithms.

A general event driven algorithm calculates the collision time for each pair of particles and, if it is under a cutoff, stores it in an event queue. It then fast forwards to the shortest time whereupon it will need to update the queue with new events that appear after the collision. Initially this requires checking all pairs, Alder and Wainwright call this the "long cycle", and this has complexity of order N-squared, O(N^2). This means that if you double the number of particles, N, then you have four times as many calculations to perform.

After a collision you only need to update events involving the particles that collided so you can get away with doing N updates. This is the "short cycle" and is O(N). It's not mentioned in this paper but I think there's an issue with sorting the event queue so this is probably still O(N^2). Either way, for their early simulations the total number of collisions per hour tanked as N was increased.

And this is where algorithms come in. You can use all sorts of tricks. In dense systems you can use a cell structure to rule out collisions between pairs far away. Modern algorithms focus largely on keeping the event queue properly sorted. A binary tree will sort with O(log(N)) and here they claim to have it O(1). Of course the complexity is not the only important factor, there may be other more important overheads, but it gives an idea of the limitations.

In equilibrium statistical mechanics specialised computer algorithms have made a spectacular impact. Techniques such as the Wolff algorithm, Umbrella sampling, and many many more, have outstripped any speed up by Moore's law by many orders of magnitude. I could go on about algorithms for hours (maybe a post brewing), instead I'll just make the point that it doesn't always pay to just sit and wait for a faster computer.

We've come a long way

These early simulation studies weren't just important for developing methods, they were able to answer some serious questions that were hopelessly out of reach at the time. Since then simulation has firmly established itself in the dance between theory and experiment, testing ideas and generating new ones. And it shows no sign of giving up that position.

Friday, 15 April 2011

Lipid membranes on the arXiv

A while ago I discussed lipid membranes and how they could exhibit critical behaviour. There were some lovely pictures on criticality on giant unilamellar vesicles (GUVs) which are sort of model cell walls. That work was done by Sarah Keller and friends in Seattle.

This morning on the arXiv I saw this new paper, also by Sarah:

Dynamic critical exponent in a 2D lipid membrane with conserved order parameter

They look at the critical dynamics of the GUV's surface. Being embedded in a 3D fluid does have its consequences so they've attempted to account for the effect of hydrodynamic interactions. I haven't poured over their model but the paper looks really nice.

Wednesday, 13 April 2011

Paper review: Hexatic phases in 2D

I'm doing my journal club on this paper by Etienne Bernard and Werner Krauth at ENS in Paris:

First-order liquid-hexatic phase transition in hard disks

So I thought that instead of making pen-and-paper notes I'd make them here so that you, my huge following, can join in. If you want we can do it proper journal club style in the comments. For now, here's my piece.

Phase transitions in 2D

Dimension two is the lowest dimension we see phase transitions. In one dimension there just aren't enough connections between the different particles – or spins, or whatever we have – to build up the necessary correlations to beat temperature. In three dimensions there are loads of paths between A and B and the correlations really get going. We get crisp phase transitions and materials will readily gain long range order. Interestingly, while it should be easier and easier to form crystals in higher dimensions there do exist pesky glass transitions that get worse with increasing dimension. But I digress.

In two dimensions slightly strange things can happen. For one thing, while we can build nice crystals they are never quite as good as the ones you can get in 3D. What do I mean by this? Well in 3D I can give you the position of one particle and then the direction of the lattice vectors and you can predict exactly where every particle in the box will sit (save a bit of thermal wiggling). In 2D we get close, if I give you the position and lattice vectors then that defines the relative position and orientation for a long way – but not everywhere.

By "a long way" I mean correlations decay algebraically (distance to the power something) rather than exponentially (something to the power distance), which would be short ranged. We can call it quasi-long ranged.

Never-the-less, this defines a solid phase and this solid can melt into a liquid (no long range order of any kind). What is very interesting in two dimensions is that this appears to happen in two stages. First the solid loses its positional order, then it loses it's orientational order as well. This is vividly demonstrated in Fig 3. of the paper. The phase in the middle, with quasi-long range orientational order but short range positional order, is known as the hexatic phase.

When the lattice is shifted a bit the orientation can be maintained but the positions become disordered.