arXiv subjects

Sharon C. Glotzer

Publications and source records attributed to Sharon C. Glotzer.

At least 37 records · Page 2Linked to original sources

arXiv2021

A Route to Hierarchical Assembly of Colloidal Diamond

Photonic crystals, appealing for their ability to control light, are constructed by periodic regions of different dielectric constants. Yet, the structural holy grail in photonic materials, diamond, remains challenging to synthesize at the colloidal length scale. Here we explore new ways to assemble diamond using modified gyrobifastigial (mGBF) nanoparticles, a shape that resembles two antialigned triangular prisms. We investigate the parameter space that leads to the self-assembly of diamond, and we compare the likelihood of defects in diamond self-assembled via mGBF vs. the nanoparticle shape that is the current focus for assembling diamond, the truncated tetrahedra. We introduce a potential route for realizing mGBF particles by dimerizing triangular prisms using attractive patches, and we report the impact of this superstructure on the photonic properties.

cond-mat.mtrl-sci↗

arXiv2021

Shape-Driven Caging Dynamics of Hard Polygons

The fundamentals of Brownian motion have been largely well understood since the early 20th century, with most recent additions focusing on understanding anomalous diffusion via rescaling of drag coefficents. That focus emphasizes long-time dynamic behavior, but recent results indicate that additional, secondary modes are also present at short and intermediate times in fluids of anisotropic particles. Here, we study the dynamics of a representative family of nearly-hard n-gons. Using molecular dynamics simulations, we study a distinct form of caging only present in anisotropic systems. We show that this caging behavior emerges in the mean squared displacement of n-gons at intermediate particle volume fractions. We then develop an extended Langevin theory directly coupling translational and rotational motion as a function of the relative anisotropy for different n-gons that predicts the observed caging behavior. Extending our theory to incorporate secondary, off-phase cross-correlations between particles further enables the prediction of both translational and rotational relaxation times of the system.

cond-mat.soft↗

arXiv2021

Formation of a single quasicrystal upon collision of multiple grains

Quasicrystals exhibit long-range order but lack translational symmetry. When grown as single crystals, they possess distinctive and unusual properties owing to the absence of grain boundaries. Unfortunately, conventional methods such as bulk crystal growth or thin film deposition only allow us to synthesize either polycrystalline quasicrystals or quasicrystals that are at most a few centimeters in size. Here, we reveal through real-time and 3D imaging the formation of a single decagonal quasicrystal arising from a hard collision between multiple growing quasicrystals in an Al-Co-Ni liquid. Through corresponding molecular dynamics simulations, we examine the underlying kinetics of quasicrystal coalescence and investigate the effects of initial misorientation between the growing quasicrystalline grains on the formation of grain boundaries. At small misorientation, coalescence occurs following rigid rotation that is facilitated by phasons. Our joint experimental-computational discovery paves the way toward fabrication of single, large-scale quasicrystals for novel applications.

cond-mat.mtrl-sci↗

arXiv2021

Microscopic Theory of Entropic Bonding for Colloidal Crystal Prediction

Entropy alone can self-assemble hard particles into colloidal crystals of remarkable complexity whose structures are the same as atomic and molecular crystals, but with larger lattice spacings. Although particle-based molecular simulation is a powerful tool for predicting self-assembly by exploring phase space, it is not yet possible to predict colloidal crystal structures a priori from particle shape as we can for atomic crystals based on electronic valency. Here we present such a first principles theory. By directly calculating and minimizing excluded volume within the framework of statistical mechanics, we describe the directional forces that emerge between hard shapes in familiar terms used to describe chemical bonds. Our theory predicts thermodynamically preferred structures for four families of hard polyhedra that match, in every instance, previous simulation results. The success of this first principles approach to entropic colloidal crystal structure prediction furthers fundamental understanding of both entropically driven crystallization and conceptual pictures of bonding in matter.

cond-mat.soft↗

arXiv2020

Muscle-inspired flexible mechanical logic architecture for colloidal robotics

Materials that respond to external stimuli by expanding or contracting provide a transduction route that integrates sensing and actuation powered directly by the stimuli. This motivates us to build colloidal scale robots using these materials that can morph into arbitrary configurations. For intelligent use of global stimuli in robotic systems, computation ability needs to be incorporated within them. The challenge is to design an architecture that is compact, material agnostic, stable under stochastic forces and can employ stimuli-responsive materials. We present an architecture that computes combinatorial logic using mechanical gates that use muscle-like response - expansion and contraction - as circuit signal with additional benefits of logic circuitry being physically flexible and able to be retrofit to arbitrary robot bodies. We mathematically analyze gate geometry and discuss tuning it for the given requirements of signal dimension and magnitude. We validate the function and stability of the design at the colloidal scale using Brownian dynamics simulations. We also demonstrate the gate design using a 3D printed model. Finally, we simulate a complete robot that folds into Tetris shapes.

cs.RO↗

arXiv2020

Randomness in self-assembled colloidal crystals can widen photonic band gaps through particle shape and internal structure

Using computer simulations, we explore how thermal noise-induced randomness in a self-assembled photonic crystal affects its photonic band gaps (PBGs). We consider a two-dimensional photonic crystal comprised of a self-assembled array of parallel dielectric hard rods of infinite length with circular or square cross section. We find the PBGs can exist over a large range of intermediate packing densities. Counterintuitively, the largest band gap does not always appear at the packing density where the crystal is most ordered, despite the randomness inherent in any self-assembled structure. For rods with square cross section at intermediate packing densities, we find that the transverse magnetic (TM) band gap of the self-assembled (i.e. thermal) system can be larger than that of identical rods arranged in a perfect square lattice. By considering hollow rods, we find the band gap of transverse electric (TE) modes can be substantially increased while that of TM modes show no obvious improvement over solid rods. Our study suggests that particle shape and internal structure can be used to engineer the PBG of a self-assembled system despite the positional and orientational randomness arising from thermal noise.

cond-mat.soft↗

arXiv2020

freud: A Software Suite for High Throughput Analysis of Particle Simulation Data

The freud Python package is a powerful library for analyzing simulation data. Written with modern simulation and data analysis workflows in mind, freud provides a Python interface to fast, parallelized C++ routines that run efficiently on laptops, workstations, and supercomputing clusters. The package provides the core tools for finding particle neighbors in periodic systems, and offers a uniform API to a wide variety of methods implemented using these tools. As such, freud users can access standard methods such as the radial distribution function as well as newer, more specialized methods such as the potential of mean force and torque and local crystal environment analysis with equal ease. While many comparable tools place a heavy emphasis on reading and operating on trajectory file formats, freud instead accepts numerical arrays of data directly as inputs. By remaining agnostic to its data source, freud is suitable for analyzing any coarse-grained particle simulation, regardless of the original data representation or simulation method. When used for on-the-fly analysis in conjunction with scriptable simulation software such as HOOMD-blue, freud enables smart simulations that adapt to the current state of the system, allowing users to study phenomena such as nucleation and growth.

physics.comp-ph↗

arXiv2019

Pinning Dislocations in Colloidal Crystals with Active Particles that Seek Stacking Faults

There is growing interest in functional, adaptive devices built from colloidal subunits of micron size or smaller. A colloidal material with dynamic mechanical properties could facilitate such microrobotic machines. Here we study via computer simulation how active interstitial particles in small quantities can be used to modify the bulk mechanical properties of a colloidal crystal. Passive interstitial particles are known to pin dislocations in metals, thereby increasing resistance to plastic deformation. We extend this tactic by employing anisotropic active interstitials that travel super-diffusively and bind strongly to stacking faults associated with partial dislocations. We find that: 1) interstitials that are effective at reducing plasticity compromise between strong binding to stacking faults and high mobility in the crystal bulk. 2) Reorientation of active interstitials in the crystal depends upon rotational transitions between high-symmetry crystal directions. 3) The addition of certain active interstitial shapes at concentrations as low as $60$ per million host particles ($0.006\%$) can create a shear threshold for dislocation migration.

cond-mat.soft↗

arXiv2019

HOOMD-blue: A Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations

HOOMD-blue is a particle simulation engine designed for nano- and colloidal-scale molecular dynamics and hard particle Monte Carlo simulations. It has been actively developed since March 2007 and available open source since August 2008. HOOMD-blue is a Python package with a high performance C++/CUDA backend that we built from the ground up for GPU acceleration. The Python interface allows users to combine HOOMD-blue with with other packages in the Python ecosystem to create simulation and analysis workflows. We employ software engineering practices to develop, test, maintain, and expand the code.

physics.comp-ph↗

arXiv2019

Looped liquid-liquid coexistence in protein crystallization

In view of the notorious complexity of protein--protein interactions, simplified models of proteins treated as patchy particles offer a promising strategy to obtain insight into the mechanism of crystallization. Here we report liquid--liquid phase separation (LLPS) with a highly asymmetric coexistence region in a computational model of rubredoxin with real molecular shape. The coexistence region terminates in both an upper (UCST) and a lower (LCST) critical solution temperature, and the complex molecular shape explains the closed-loop behavior of the LLPS.

cond-mat.soft↗

arXiv2019

Energy Landscapes for Digital Alchemy

We apply energy landscape methods to digital alchemy, defining a system in which the parameters of the potential are treated as degrees of freedom. Using geometrical optimisation, we locate minima and transition states on the landscape for small clusters. We show that it is easy to find the parameters that give the lowest energy minimum, and that the distribution of minima on the alchemical landscape is concentrated in particular areas. We also conclude that the alchemical landscape is more frustrated, in terms of competition between low energy structures separated by high barriers. Transition states on the alchemical landscape are classified by whether they become minima or transition states when the potential parameters are fixed. Those that become minima have a significant alchemical component, while those that remain as transition states can be characterised mainly in terms of atomic displacements.

cond-mat.soft↗

arXiv2019

Topological Order in Densely Packed Anisotropic Colloids

The existence of topological order is frequently associated with strongly coupled quantum matter. Here, we demonstrate the existence of topological phases in classical systems of densely packed, hard, anisotropic polyhedrally shaped colloidal particles. We show that previously reported transitions in dense packings lead to the existence of topologically ordered thermodynamic phases, which we show are stable away from the dense packing limit. Our work expands the library of known topological phases, whose experimental realization could provide new means for constructing plasmonic materials that are robust in the presence of fluctuations.

cond-mat.soft↗

arXiv2019

FCC-to-BCC phase transitions in convex and concave hard particle systems

Particle shape plays an important role in the phase behavior of colloidal self-assembly. Recent progress in particle synthesis has made particles of polyhedral shapes and dimpled spherical shapes available. Here using computer simulations of hard particle models, we study face-centered cubic to body-centered cubic (FCC-to-BCC) phase transitions in a convex 432 polyhedral shape family and a concave dimpled sphere family. Particles in both families have four-, three-, and two-fold rotational symmetries. Via free energy calculations we find the FCC-to-BCC transitions in both families are first order. As a previous work reports the FCC-to-BCC phase transition is first order in a convex 332 family of hard polyhedra, our work provides additional insight into the FCC-to-BCC transition and how the convexity or concavity of particle shape affects phase transition pathways.

cond-mat.soft↗

arXiv2018

Inverse Design of Simple Pair Potentials for the Self-Assembly of Complex Structures

The synthesis of complex materials through the self-assembly of particles at the nanoscale provides opportunities for the realization of novel material properties. However, the inverse design process to create experimentally feasible interparticle interaction strategies is uniquely challenging. Standard methods for the optimization of isotropic pair potentials tend toward overfitting, resulting in solutions with too many features and length scales that are challenging to map to mechanistic models. Here we introduce a method for the optimization of simple pair potentials that minimizes the relative entropy of the complex target structure while directly considering only those length scales most relevant for self-assembly. Our approach maximizes the relative information of a target pair distribution function with respect to an ansatz distribution function via an iterative update process. During this process, we filter high frequencies from the Fourier spectrum of the pair potential, resulting in interaction potentials that are smoother and simpler in real space, and therefore likely easier to make. We show that pair potentials obtained by this method assemble their target structure more robustly with respect to optimization method parameters than potentials optimized without filtering.

cond-mat.soft↗

arXiv2018

Phase separation of self-propelled ballistic particles

Self-propelled particles phase separate into coexisting dense and dilute regions above a critical density. The statistical nature of their stochastic motion lends itself to various theories that predict the onset of phase separation. However, these theories are ill equipped to describe such behavior when noise become negligible. To overcome this limitation, we present a predictive model that relies on two density-dependent timescales: $τ_F$, the mean time particles spend between collisions; and $τ_C$, the mean lifetime of a collision. We show that only when $τ_F < τ_C$ do collisions last long enough to develop a growing cluster and initiate phase separation. Using both analytical calculations and active particle simulations, we measure these timescales and determine the critical density for phase separation in both 2D and 3D.

cond-mat.soft↗

arXiv2018

Pressure-Tunable Photonic Band Gaps in an Entropic Colloidal Crystal

Materials adopting the diamond structure possess useful properties in atomic and colloidal systems, and are a popular target for synthesis in colloids where a photonic band gap is possible. The desirable photonic properties of the diamond structure pose an interesting opportunity for reconfigurable matter: can we create a colloidal crystal able to switch reversibly to and from the diamond structure? Drawing inspiration from high-pressure transitions of diamond-forming atomic systems, we design a system of polyhedrally-shaped particles that transitions from diamond to a tetragonal diamond derivative upon a small pressure change. The transition can alternatively be triggered by changing the shape of the particle in-situ. We propose that the transition provides a reversible reconfiguration process for a potential new colloidal material, and draw parallels between this transition and phase behavior of the atomic transitions from which we take inspiration.

cond-mat.mtrl-sci↗

arXiv2018

Mapping Disorder in Entropically Ordered Crystals

Systems of hard shapes crystallize due to entropy. How is entropy distributed among translational and rotational microscopic contributions? We answer this question by decomposing thermal fluctuation of crystals of hard hexagons into collective modes, a generalization and quantification of the Onsager picture of hard rod liquid crystals. We show that at densities both near densest packing and near the solid-hexatic melting transition, solids of hard regular hexagons hold most of their entropy in translational degrees of freedom.

cond-mat.soft↗

arXiv2018

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.

physics.comp-ph↗