Dynamical heterogeneity and jamming in glass-forming liquids

The relationship between spatially heterogeneous dynamics (SHD) and jamming is studied in a glass-forming binary Lennard-Jones system via molecular dynamics simulations. It has been suggested that the probability distribution of interparticle forces $P(F)$ develops a peak at the glass transition temperature $T_g$, and that the large force inhomogeneities, responsible for structural arrest in granular materials, are related to dynamical heterogeneities in supercooled liquids that form glasses. It has been further suggested that ``force chains'' present in granular materials may exist in supercooled liquids, and may provide an order parameter for the glass transition. Our goal is to investigate the extent to which the forces experienced by particles in a glass-forming liquid are related to SHD, and compare these forces to those observed in granular materials and other glass-forming systems. We find no peak in $P(F)$ at any temperature in our system, even below $T_g$. We also find that particles that have been localized for a long time are less likely to experience high relative force and that mobile particles experience higher relative forces at shorter time scales, indicating a correlation between pairwise forces and particle mobility. We also discuss a possible relationship between force chains found here and the development of string-like motion found in other glass-forming liquids.Comment: 18 pages, 14 figure

Quantifying Spatially Heterogeneous Dynamics in Computer Simulations of Glassforming Liquids

We examine the phenomenon of dynamical heterogeneity in computer simulations of an equilibrium, glass-forming liquid. We describe several approaches to quantify the spatial correlation of single-particle motion, and show that spatial correlations of particle displacements become increasingly long-range as the temperature decreases toward the mode coupling critical temperature.Comment: To appear in Journal of Physics: Condensed Matte

Shapes within shapes: how particles arrange inside a cavity

We calculate the configurational entropy of hard particles confined in a cavity using Monte Carlo integration. Multiple combinations of particle and cavity shapes are considered. For small numbers of particles $N$, we show that the entropy decreases monotonically with increasing cavity aspect ratio, regardless of particle shape. As $N$ increases, we find ordered regions of high and low particle density, with the highest density near the boundary for all particle and cavity shape combinations. Our findings provide insights relevant to engineering particles in confined spaces, entropic barriers, and systems with depletion interactions.Comment: 6 pages, 8 figure

On the Mechanism of Pinning in Phase-Separating Polymer Blends

We re-explore the kinetics of spinodal decomposition in off-critical polymer blends through numerical simulations of the Cahn-Hilliard equation with the Flory-Huggins-De Gennes free energy functional. Even in the absence of thermal noise, the solution of the discretized equation of motion shows coarsening in the late stages of spinodal decomposition without evidence of pinning, regardless of the relative concentration of the blend components. This suggests this free energy functional is not sufficient to describe the physics responsible for pinning in real blends.Comment: 20 pages, latex, 4 uuencoded figures. Accepted for publication in J. Chem. Phy

Structural signatures of strings and propensity for mobility in a simulated supercooled liquid above the glass transition

By molecular dynamics (MD) simulation of the one-component Dzugutov liquid in a metastable equilibrium supercooled state approaching the glass transition, we investigate the structural properties of highly mobile particles moving in strings at low temperature T where string-like particle motion (SLM) is well developed. We find that SLM occurs most frequently in the boundary regions between clusters of icosahedrally-ordered particles and disordered, liquid-like, domains. Further, we find that the onset T for significant SLM coincides with the T at which clusters of icosahedrally-ordered particles begin to appear in considerable amounts, which in turn coincides with the onset T for non-Arrhenius dynamics. We find a unique structural environment for strings that is different from the structure of the bulk liquid at any T. This unique string environment persists from the melting T upon cooling to the lowest T studied in the vicinity of the mode-coupling temperature, and is explained by the existence of rigid elongated cages. We also form a criterion based solely on structural features of the local environment that allow the identification of particles with an increased propensity for mobility

Efficient Phase Diagram Sampling by Active Learning

We address the problem of efficient phase diagram sampling by adopting active learning techniques from machine learning, and achieve an 80% reduction in the sample size (number of sampled statepoints) needed to establish the phase boundary up to a given precision in example application. Traditionally, data is collected on a uniform grid of predetermined statepoints. This approach, also known as grid search in the machine learning community, suffers from low efficiency by sampling statepoints that provide no information about the phase boundaries. We propose an active learning approach to overcome this deficiency by adaptively choosing the next most informative statepoint(s) every round. This is done by interpolating the sampled statepoints' phases by Gaussian Process regression. An acquisition function quantifies the informativeness of possible next statepoints, maximizing the information content in each subsequently sampled statepoint. We also generalize our approach with state-of-the-art batch sampling techniques to better utilize parallel computing resources. We demonstrate the usefulness of our approach in a few example simulations relevant to soft matter physics, although our algorithms are general. Our active learning enhanced phase diagram sampling method greatly accelerates research and opens up opportunities for extra-large scale exploration of a wide range of phase diagrams by simulations or experiments

Predicting colloidal crystals from shapes via inverse design and machine learning

A fundamental challenge in materials design is linking building block attributes to crystal structure. Addressing this challenge is particularly difficult for systems that exhibit emergent order, such as entropy-stabilized colloidal crystals. We combine recently developed techniques in inverse design with machine learning to construct a model that correctly classifies the crystals of more than ten thousand polyhedral shapes into 13 different structures with a predictive accuracy of 96% using only two geometric shape measures. With three measures, 98% accuracy is achieved. We test our model on previously reported colloidal crystal structures for 71 symmetric polyhedra and obtain 92% accuracy. Our findings (1) demonstrate that entropic colloidal crystals are controlled by surprisingly few parameters, (2) provide a quantitative model to predict these crystals solely from the geometry of their building blocks, and (3) suggest a prediction paradigm that easily generalizes to other self-assembled materials.Comment: 4 figure

A parallel algorithm for implicit depletant simulations

We present an algorithm to simulate the many-body depletion interaction between anisotropic colloids in an implicit way, integrating out the degrees of freedom of the depletants, which we treat as an ideal gas. Because the depletant particles are statistically independent and the depletion interaction is short-ranged, depletants are randomly inserted in parallel into the excluded volume surrounding a single translated and/or rotated colloid. A configurational bias scheme is used to enhance the acceptance rate. The method is validated and benchmarked both on multi-core CPUs and graphics processing units (GPUs) for the case of hard spheres, hemispheres and discoids. With depletants, we report novel cluster phases, in which hemispheres first assemble into spheres, which then form ordered hcp/fcc lattices. The method is significantly faster than any method without cluster moves and that tracks depletants explicitly, for systems of colloid packing fraction $\phi_c<0.50$, and additionally enables simulation of the fluid-solid transition.Comment: 10 pages, 8 figure

Using Depletion to Control Colloidal Crystal Assemblies of Hard Cuboctahedra

Depletion interactions arise from entropic forces, and their ability to induce aggregation and even ordering of colloidal particles through self-assembly is well established, especially for spherical colloids. We vary the size and concentration of penetrable hard sphere depletants in a system of cuboctahedra, and we show how depletion changes the preferential facet alignment of the colloids and thereby selects different crystal structures. Moreover, we explain the cuboctahedra phase behavior using perturbative free energy calculations. We find that cuboctahedra can form a stable simple cubic phase, and, remarkably, that the stability of this phase can only be rationalized by considering the effects of both the colloid and depletant entropy. We corroborate our results by analyzing how the depletant concentration and size affect the emergent directional entropic forces and hence the effective particle shape. We propose the use of depletants as a means of easily changing the effective shape of self-assembling anisotropic colloids

Structural diversity and the role of particle shape and dense fluid behavior in assemblies of hard polyhedra

A fundamental characteristic of matter is its ability to form ordered structures under the right thermodynamic conditions. Predicting these structures - and their properties - from the attributes of a material's building blocks is the holy grail of materials science. Here, we investigate the self-assembly of 145 hard convex polyhedra whose thermodynamic behavior arises solely from their anisotropic shape. Our results extend previous works on entropy-driven crystallization by demonstrating a remarkably high propensity for self-assembly and an unprecedented structural diversity, including some of the most complex crystalline phases yet observed in a non-atomic system. In addition to 22 Bravais and non-Bravais crystals, we report 66 plastic crystals (both Bravais and topologically close-packed), 21 liquid crystals (nematic, smectic, and columnar), and 44 glasses. We show that from simple measures of particle shape and local order in the disordered fluid, the class of ordered structure can be predicted.Comment: 21 pages, 4 figure