Moving beyond the constraints of chemistry via crystal structure discovery with isotropic multiwell pair potentials.

The rigid constraints of chemistry-dictated by quantum mechanics and the discrete nature of the atom-limit the set of observable atomic crystal structures. What structures are possible in the absence of these constraints? Here, we systematically crystallize one-component systems of particles interacting with isotropic multiwell pair potentials. We investigate two tunable families of pairwise interaction potentials.

Nonlinear machine learning and design of reconfigurable digital colloids.

Digital colloids, a cluster of freely rotating "halo" particles tethered to the surface of a central particle, were recently proposed as ultra-high density memory elements for information storage. Rational design of these digital colloids for memory storage applications requires a quantitative understanding of the thermodynamic and kinetic stability of the configurational states within which information is stored. We apply nonlinear machine learning to Brownian dynamics simulations of these digital colloids to extract the low-dimensional intrinsic manifold governing digital colloid morphology, thermodynamics, and kinetics.

Tracking vortices in superconductors: Extracting singularities from a discretized complex scalar field evolving in time.

In type-II superconductors, the dynamics of magnetic flux vortices determine their transport properties. In the Ginzburg-Landau theory, vortices correspond to topological defects in the complex order parameter field. Earlier, in Phillips et al.

Detecting vortices in superconductors: extracting one-dimensional topological singularities from a discretized complex scalar field.

In type II superconductors, the dynamics of superconducting vortices determine their transport properties. In the Ginzburg-Landau theory, vortices correspond to topological defects in the complex order parameter. Extracting their precise positions and motion from discretized numerical simulation data is an important, but challenging, task.

Computational self-assembly of a one-component icosahedral quasicrystal.

Icosahedral quasicrystals (IQCs) are a form of matter that is ordered but not periodic in any direction. All reported IQCs are intermetallic compounds and either of face-centred-icosahedral or primitive-icosahedral type, and the positions of their atoms have been resolved from diffraction data. However, unlike axially symmetric quasicrystals, IQCs have not been observed in non-atomic (that is, micellar or nanoparticle) systems, where real-space information would be directly available.

Digital colloids: reconfigurable clusters as high information density elements.

Through the design and manipulation of discrete, nanoscale systems capable of encoding massive amounts of information, the basic components of computation are open to reinvention. These components will enable tagging, memory storage, and sensing in unusual environments - elementary functions crucial for soft robotics and "wet computing". Here we show how reconfigurable clusters made of N colloidal particles bound flexibly to a central colloidal sphere have the capacity to store an amount of information that increases as O(N ln(N)).

Phase behavior and complex crystal structures of self-assembled tethered nanoparticle telechelics.

Motivated by growing interest in the self-assembly of nanoparticles for applications such as photonics, organic photovoltaics, and DNA-assisted designer crystals, we explore the phase behavior of tethered spherical nanoparticles. Here, a polymer tether is used to geometrically constrain a pair of nanoparticles creating a tethered nanoparticle "telechelic". Using simulation, we examine how varying architectural features, such as the size ratio of the two end-group nanospheres and the length of the flexible tether, affects the self-assembled morphologies.

Self-assembled clusters of spheres related to spherical codes.

We consider the thermodynamically driven self-assembly of spheres onto the surface of a central sphere. This assembly process forms self-limiting, or terminal, anisotropic clusters (N-clusters) with well-defined structures. We use Brownian dynamics to model the assembly of N-clusters varying in size from two to twelve outer spheres and free energy calculations to predict the expected cluster sizes and shapes as a function of temperature and inner particle diameter.

Optimal filling of shapes.

We present filling as a type of spatial subdivision problem similar to covering and packing. Filling addresses the optimal placement of overlapping objects lying entirely inside an arbitrary shape so as to cover the most interior volume. In n-dimensional space, if the objects are polydisperse n-balls, we show that solutions correspond to sets of maximal n-balls.

Effect of nanoparticle polydispersity on the self-assembly of polymer tethered nanospheres.

Recent simulations predict that aggregating nanospheres functionalized with polymer "tethers" can self-assemble to form a cylinder, perforated lamellae, lamellae, and even the double gyroid phase, which are phases also seen in block copolymer and surfactant systems. Nanoparticle size polydispersity is likely to be a characteristic of these systems. If too high, polydispersity may destabilize a phase.

A two-temperature model of radiation damage in α-quartz.

Two-temperature models are used to represent the physics of the interaction between atoms and electrons during thermal transients such as radiation damage, laser heating, and cascade simulations. We introduce a two-temperature model applied to an insulator, α-quartz, to model heat deposition in a SiO(2) lattice. Our model of the SiO(2) electronic subsystem is based on quantum simulations of the electronic response in a SiO(2) repeat cell.

An energy-conserving two-temperature model of radiation damage in single-component and binary Lennard-Jones crystals.

Two-temperature models are used to represent the interaction between atoms and free electrons during thermal transients such as radiation damage, laser heating, and cascade simulations. In this paper, we introduce an energy-conserving version of an inhomogeneous finite reservoir two-temperature model using a Langevin thermostat to communicate energy between the electronic and atomic subsystems. This energy-conserving modification allows the inhomogeneous two-temperature model to be used for longer and larger simulations and simulations of small energy phenomena, without introducing nonphysical energy fluctuations that may affect simulation results.