Development of efficient molecular simulation techniques to investigate the dynamics of dense colloidal systems

Colloids have a striking relevance in a wide spectrum of industrial formulations, spanning from personal care products to protective paints. Their behaviour can be easily influenced by extremely weak forces, which disturb their thermodynamic equilibrium and dramatically determine their performance.

Modelling colloids is especially challenging as their physics develop over large time and length scales, usually up to micro-seconds and micro-meters, respectively. As such, to fully unveil their behaviour, one should neglect many specific details and retain only few key ingredients. Conventional simulation techniques, such as MD, where the trajectories of all colloidal particles and solvent molecules are tracked, would not allow to capture the phenomena occurring at long-time scales in a sufficiently large sample of matter. Our Dynamic Monte Carlo (DMC) simulation technique, where the effect of the solvent is incorporated as effective inter-particle interactions, is able to quantitatively reproduce the Brownian motion of colloids in excellent agreement with the more computationally demanding Brownian Dynamics (BD) simulation. However, the DMC technique has still important limitations and cannot be currently applied to describe density heterogeneities. The present proposal aims to bridge this gap and apply the new DMC approach to better understand the effect of
ordered crowding on macromolecular diffusion.

Our DMC algorithm can successfully mimic the Brownian motion of pure systems [1] and mixtures [2] of colloidal particles in isotropic, nematic and smectic liquid crystal phases. By rescaling the MC time step with the acceptance ratio of particle displacements and rotations, we demonstrated the existence of a unique MC time scale that allows for a direct comparison with Brownian Dynamics (BD) simulations. This theory has been very recently generalised to reproduce the Brownian motion of colloidal particles during transitory unsteady states, when their thermodynamic equilibrium is significantly modified [3]. In particular, from a steady-state condition of dynamic equilibrium, where all the observables, including the above mentioned acceptance ratio are independent of time, the system undergoes a transitory unsteady state taking it to a new equilibrium configuration. Further generalising our DMC algorithm to processes displaying significant density fluctuations, such as nucleation and growth, where the MC acceptance ratio is expected to depend on both time and space, is the aim of this research project.