Predicting the self-assembly of a model colloidal crystal

We investigate the self-assembly (crystallisation) of particles with hard cores and isotropic, square-well interactions, using a Monte Carlo scheme to simulate overdamped Langevin dynamics. We measure correlation and response functions during the early stages of assembly, and we analyse the results using fluctuation-dissipation theorems, aiming to predict which systems will self-assemble successfully and which will get stuck in disordered states. The early-time correlation and response measurements are made before significant crystallisation has taken place, indicating that dynamical measurements are valuable in measuring a system's propensity for kinetic trapping

Complexity in surfaces of densest packings for families of polyhedra

Packings of hard polyhedra have been studied for centuries due to their mathematical aesthetic and more recently for their applications in fields such as nanoscience, granular and colloidal matter, and biology. In all these fields, particle shape is important for structure and properties, especially upon crowding. Here, we explore packing as a function of shape. By combining simulations and analytic calculations, we study three 2-parameter families of hard polyhedra and report an extensive and systematic analysis of the densest packings of more than 55,000 convex shapes. The three families have the symmetries of triangle groups (icosahedral, octahedral, tetrahedral) and interpolate between various symmetric solids (Platonic, Archimedean, Catalan). We find that optimal (maximum) packing density surfaces that reveal unexpected richness and complexity, containing as many as 130 different structures within a single family. Our results demonstrate the utility of thinking of shape not as a static property of an object in the context of packings, but rather as but one point in a higher dimensional shape space whose neighbors in that space may have identical or markedly different packings. Finally, we present and interpret our packing results in a consistent and generally applicable way by proposing a method to distinguish regions of packings and classify types of transitions between them.Comment: 16 pages, 8 figure

Controlling crystal self-assembly using a real-time feedback scheme

We simulate crystallisation of hard spheres with short-ranged attractive potentials as a model self-assembling system. Using measurements of correlation and response functions, we develop a method whereby the interaction parameters between the particles are automatically tuned during the assembly process, in order to obtain high-quality crystals and avoid kinetic traps. The method we use is independent of the details of the interaction potential and of the structure of the final crystal - we propose that it can be applied to a wide range of self-assembling systems

Digital Alchemy for Materials Design: Colloids and Beyond

Starting with the early alchemists, a holy grail of science has been to make desired materials by modifying the attributes of basic building blocks. Building blocks that show promise for assembling new complex materials can be synthesized at the nanoscale with attributes that would astonish the ancient alchemists in their versatility. However, this versatility means that making direct connection between building block attributes and bulk behavior is both necessary for rationally engineering materials, and difficult because building block attributes can be altered in many ways. Here we show how to exploit the malleability of the valence of colloidal nanoparticle "elements" to directly and quantitatively link building block attributes to bulk behavior through a statistical thermodynamic framework we term "digital alchemy". We use this framework to optimize building blocks for a given target structure, and to determine which building block attributes are most important to control for self assembly, through a set of novel thermodynamic response functions, moduli and susceptibilities. We thereby establish direct links between the attributes of colloidal building blocks and the bulk structures they form. Moreover, our results give concrete solutions to the more general conceptual challenge of optimizing emergent behaviors in nature, and can be applied to other types of matter. As examples, we apply digital alchemy to systems of truncated tetrahedra, rhombic dodecahedra, and isotropically interacting spheres that self assemble diamond, FCC, and icosahedral quasicrystal structures, respectively.Comment: 17 REVTeX pages, title fixed to match journal versio

Clusters of polyhedra in spherical confinement

What is the best way to pack objects into a container? This simple question, one that is relevant to everyday life, biology, and nanoscience, is easy to state but surprisingly difficult to answer. Here, we use computational methods to determine dense packings of a set of polyhedra inside a sphere, for up to 60 constituent packers. Our dense packings display a wide variety of symmetries and structures, and indicate that the presence of the spherical container suppresses packing effects due to polyhedral shape. Our results have implications for a range of biological phenomena and experimental applications, including blood clotting, cell aggregation, drug delivery, colloidal engineering, and the creation of metamaterials