Crystal nucleation and growth of monodisperse hard-spheres as a function of packing density is studied by collision-driven molecular dynamics simulations. Short-range order in the form of fivefold local symmetry is identified and its dynamical and structural evolution is tracked as the originally amorphous assembly transits to the stable ordered phase. A cluster-based approach shows that hardsphere configurations having initially a similar average fraction of fivefold and ordered sites can crystallize in completely different patterns both in terms of dynamics and morphology. It is found that at high volume fractions crystallization is significantly delayed in assemblies where sites with fivefold symmetry are abundant. Eventually, once the crystal phase is reached, fivefold symmetry either diminishes or arranges in specific geometric patterns. Such defects are spatially strongly correlated with twinning planes at crystalline boundaries. A detailed analysis is provided on the structural characteristics of the established crystal morphologies.

Through molecular simulations we investigate the dynamics of crystallization of hard spheres of uniform size from dense amorphous states and the role that hidden structures in an otherwise disordered medium might have on it. It is shown that short-range order in the form of sites with fivefold symmetry acts as a powerful inhibitor to crystal growth. Fivefold sites not only retard crystallization, but can self-assemble into organized structures that arrest crystallization at high densities or lead to the formation of defects in a crystal. The latter effect can be understood in terms of a random polyhedral model.

The structural evolution of surface gratings on a glassy material is investigated by means of molecular simulations. The gratings provide a means to probe surface diffusion in the vicinity of the glass transition temperature. A theory by Mullins [J. Appl. Phys. 30, 77 (1959)] is used to extract qu-antitative measures of surface diffusivity that rely on calculation of grating amplitude as a function of time. The simulations are implemented in the context of a model binary glass mixture [S. S. Ashwin and S. Sastry, J. Phys.: Condens. Matter 15, S1253 (2003)]. We find that surface diffusion is faster than bulk diffusion by several orders of magnitude, consistent with recent experimental data for an organic glass former. The diffusivities extracted by the grating-decay approach are consistent with those estimated on the basis of mean-squared particle displacements. The grating-decay approach, however, is more efficient than traditional techniques based on Einstein's diffusion equation. Grating decay is also more versatile and is shown to be applicable in a variety of sample geometries.

Density of states Monte Carlo simulations have been performed to study the isotropic-nematic (IN) transition of the Lebwohl-Lasher model for liquid crystals. The IN transition temperature was calculated as a function of system size using expanded ensemble density of states simulations with histogram reweighting. The IN temperature for infinite system size was obtained by extrapolation of three independent measures. A subsequent analysis of the kinetics in the model showed that the transition occurs via spinodal decomposition through aggregation of clusters of liquid crystal molecules.

The free energy surfaces of a wide variety of systems encountered in physics, chemistry, and biology are characterized by the existence of deep minima separated by numerous barriers. One of the central aims of recent research in computational chemistry and physics has been to determine how transitions occur between deep local minima on rugged free energy landscapes, and transition path sampling (TPS) Monte-Carlo methods have emerged as an effective means for numerical investigation of such transitions. Many of the shortcomings of TPS-like approaches generally stem from their high computational demands. Two new algorithms are presented in this work that improve the efficiency of TPS simulations. The first algorithm uses biased shooting moves to render the sampling of reactive trajectories more efficient. The second algorithm is shown to substantially improve the accuracy of the transition state ensemble by introducing a subset of local transition path simulations in the transition state. The system considered in this work consists of a two-dimensional rough energy surface that is representative of numerous systems encountered in applications. When taken together, these algorithms provide gains in efficiency of over two orders of magnitude when compared to traditional TPS simulations.