Local structure in deeply supercooled liquids exhibits growing lengthscales and dynamical correlations

Computer simulations are very powerful: in the case of molecular dynamics, we can model the positions and velocities of atoms or molecules and observe the emergence of pattern and structures in situ, following each individual atom in its trajectory.

However, when we study supercooled liquids or glasses, it is hard to probe in computer simulations very low temperatures or very tightly packed systems, unless we opt for indirect and clever routes to glean some information on the low temperature behaviour. It would be great if one could directly take a very cold (or, similarly, very dense) liquid at equilibrium and see how the constituent particles are arranged.

This is precisely what James Hallett has managed to do during his stay in Bristol using super-resolution microscopy: this method can access the coordinates of equilibrium themal packings so dense that a direct simulation would never do. Thanks to James’s clever imaging, we have then carefully analysed the individual coordinates and trajectories of dense repulsive colloids and managed to clearly show how the local environment of these densely packed equilibrium systems changes as the density is increased.

We have found some notable features: as we take denser samples, the liquid becomes gradually richer in regions where particles are arranged into five-fold symmetric structures; those regions display reduced mobility compared to other regions of the sample; randomly selected domains of the system become more and more “similar to each other” as the density is increased, and such an increase in density can be understood as a decrease in the number of distinct configurations the liquid can take, a quantity related to its so-called configurational entropy.

This work has just appeared in Nature Communications. Full text here:

James E. Hallett, Francesco Turci, and C. Patrick Royall. Nature Communications 9(1), 3272 (2018).

 

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Measuring the similarity between randomly sampled regions: (a) picking at random a blue and yellow group of particles from the (beautiful) experimental coordinates we can use methods based on singular value decomposition (SVD) to find the best-fitting rotation and translation. The overlap Q between blue and yellow particles is then calculated. In (b) (c) and (d) we see how the probability distribution of the overalap changes as (b) we change the volume fraction φ; (c) when we look at 5-fold symmetric regions (LFS-rich) at low φ and high φ. Only at high φ LFS-rich regions are significantly more similar.

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