arXiv subjects

Joshua A. Anderson

Publications and source records attributed to Joshua A. Anderson.

13 recordsLinked to original sources

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

Newtonian Event-Chain Monte Carlo and Collision Prediction with Polyhedral Particles

Polyhedral nanocrystals are building blocks for nanostructured materials that find applications in catalysis and plasmonics. Synthesis efforts and self-assembly experiments have been assisted by computer simulations that predict phase equilibra. Most current simulations employ Monte Carlo methods, which generate stochastic dynamics. Collective and correlated configuration updates are alternatives that promise higher computational efficiency and generate trajectories with realistic dynamics. One such alternative involves event-chain updates and has recently been proposed for spherical particles. In this contribution, we develop and apply event-chain Monte Carlo for hard convex polyhedra. Our simulation makes use of an improved computational geometry algorithm XenoSweep, which predicts sweep collision in a particularly simple way. We implement Newtonian event chains in the open source general-purpose particle simulation toolkit HOOMD-blue for serial and parallel simulation. The speed-up over state-of-the-art Monte Carlo is between a factor of 10 for nearly spherical polyhedra and a factor of 2 for highly aspherical polyhedra. Finally, we validate the Newtonian event-chain algorithm by applying it to a current research problem, the multi-step nucleation of two classes of hard polyhedra.

cond-mat.stat-mech↗

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

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↗

arXiv2016

Shape and symmetry determine two-dimensional melting transitions of hard regular polygons

The melting transition of two-dimensional (2D) systems is a fundamental problem in condensed matter and statistical physics that has advanced significantly through the application of computational resources and algorithms. 2D systems present the opportunity for novel phases and phase transition scenarios not observed in 3D systems, but these phases depend sensitively on the system and thus predicting how any given 2D system will behave remains a challenge. Here we report a comprehensive simulation study of the phase behavior near the melting transition of all hard regular polygons with $3\leq n\leq 14$ vertices using massively parallel Monte Carlo simulations of up to one million particles. By investigating this family of shapes, we show that the melting transition depends upon both particle shape and symmetry considerations, which together can predict which of three different melting scenarios will occur for a given $n$. We show that systems of polygons with as few as seven edges behave like hard disks; they melt continuously from a solid to a hexatic fluid and then undergo a first-order transition from the hexatic phase to the fluid phase. We show that this behavior, which holds for all $7\leq n\leq 14$, arises from weak entropic forces among the particles. Strong directional entropic forces align polygons with fewer than seven edges and impose local order in the fluid. These forces can enhance or suppress the discontinuous character of the transition depending on whether the local order in the fluid is compatible with the local order in the solid. As a result, systems of triangles, squares, and hexagons exhibit a KTHNY-type continuous transition between fluid and hexatic, tetratic, and hexatic phases, respectively, and a continuous transition from the appropriate "x"-atic to the solid. [abstract truncated due to arxiv length limitations].

cond-mat.soft↗

arXiv2016

GPU Accelerated Discrete Element Method (DEM) Molecular Dynamics for Conservative, Faceted Particle Simulations

Faceted shapes, such as polyhedra, are commonly found in systems of nanoscale, colloidal, and granular particles. Many interesting physical phenomena, like crystal nucleation and growth, vacancy motion, and glassy dynamics are challenging to model in these systems because they require detailed dynamical information at the individual particle level. Within the granular materials community the Discrete Element Method has been used extensively to model systems of anisotropic particles under gravity, with friction. We provide an implementation of this method intended for simulation of hard, faceted nanoparticles, with a conservative Weeks-Chandler-Andersen (WCA) interparticle potential, coupled to a thermodynamic ensemble. This method is a natural extension of classical molecular dynamics and enables rigorous thermodynamic calculations for faceted particles.

physics.comp-ph↗

arXiv2015

Scalable Metropolis Monte Carlo for simulation of hard shapes

We design and implement HPMC, a scalable hard particle Monte Carlo simulation toolkit, and release it open source as part of HOOMD-blue. HPMC runs in parallel on many CPUs and many GPUs using domain decomposition. We employ BVH trees instead of cell lists on the CPU for fast performance, especially with large particle size disparity, and optimize inner loops with SIMD vector intrinsics on the CPU. Our GPU kernel proposes many trial moves in parallel on a checkerboard and uses a block-level queue to redistribute work among threads and avoid divergence. HPMC supports a wide variety of shape classes, including spheres / disks, unions of spheres, convex polygons, convex spheropolygons, concave polygons, ellipsoids / ellipses, convex polyhedra, convex spheropolyhedra, spheres cut by planes, and concave polyhedra. NVT and NPT ensembles can be run in 2D or 3D triclinic boxes. Additional integration schemes permit Frenkel-Ladd free energy computations and implicit depletant simulations. In a benchmark system of a fluid of 4096 pentagons, HPMC performs 10 million sweeps in 10 minutes on 96 CPU cores on XSEDE Comet. The same simulation would take 7.6 hours in serial. HPMC also scales to large system sizes, and the same benchmark with 16.8 million particles runs in 1.4 hours on 2048 GPUs on OLCF Titan.

cond-mat.soft↗

arXiv2015

Shape Allophiles Improve Entropic Assembly

We investigate a class of "shape allophiles" that fit together like puzzle pieces as a method to access and stabilize desired structures by controlling directional entropic forces. Squares are cut into rectangular halves, which are shaped in an allophilic manner with the goal of re-assembling the squares while self-assembling the square lattice. We examine the assembly characteristics of this system via the potential of mean force and torque, and the fraction of particles that entropically bind. We generalize our findings and apply them to self-assemble triangles into a square lattice via allophilic shaping. Through these studies we show how shape allophiles can be useful in assembling and stabilizing desired phases with appropriate allophilic design.

cond-mat.soft↗

arXiv2014

Strong scaling of general-purpose molecular dynamics simulations on GPUs

We describe a highly optimized implementation of MPI domain decomposition in a GPU-enabled, general-purpose molecular dynamics code, HOOMD-blue (Anderson and Glotzer, arXiv:1308.5587). Our approach is inspired by a traditional CPU-based code, LAMMPS (Plimpton, J. Comp. Phys. 117, 1995), but is implemented within a code that was designed for execution on GPUs from the start (Anderson et al., J. Comp. Phys. 227, 2008). The software supports short-ranged pair force and bond force fields and achieves optimal GPU performance using an autotuning algorithm. We are able to demonstrate equivalent or superior scaling on up to 3,375 GPUs in Lennard-Jones and dissipative particle dynamics (DPD) simulations of up to 108 million particles. GPUDirect RDMA capabilities in recent GPU generations provide better performance in full double precision calculations. For a representative polymer physics application, HOOMD-blue 1.0 provides an effective GPU vs. CPU node speed-up of 12.5x.

physics.comp-ph↗

arXiv2013

Massively parallel Monte Carlo for many-particle simulations on GPUs

Current trends in parallel processors call for the design of efficient massively parallel algorithms for scientific computing. Parallel algorithms for Monte Carlo simulations of thermodynamic ensembles of particles have received little attention because of the inherent serial nature of the statistical sampling. In this paper, we present a massively parallel method that obeys detailed balance and implement it for a system of hard disks on the GPU. We reproduce results of serial high-precision Monte Carlo runs to verify the method. This is a good test case because the hard disk equation of state over the range where the liquid transforms into the solid is particularly sensitive to small deviations away from the balance conditions. On a Tesla K20, our GPU implementation executes over one billion trial moves per second, which is 148 times faster than on a single Intel Xeon E5540 CPU core, enables 27 times better performance per dollar, and cuts energy usage by a factor of 13. With this improved performance we are able to calculate the equation of state for systems of up to one million hard disks. These large system sizes are required in order to probe the nature of the melting transition, which has been debated for the last forty years. In this paper we present the details of our computational method, and discuss the thermodynamics of hard disks separately in a companion paper.

physics.comp-ph↗

arXiv2013

Hard-disk equation of state: First-order liquid-hexatic transition in two dimensions with three simulation methods

We report large-scale computer simulations of the hard-disk system at high densities in the region of the melting transition. Our simulations reproduce the equation of state, previously obtained using the event-chain Monte Carlo algorithm, with a massively parallel implementation of the local Monte Carlo method and with event-driven molecular dynamics. We analyze the relative performance of these simulation methods to sample configuration space and approach equilibrium. Our results confirm the first-order nature of the melting phase transition in hard disks. Phase coexistence is visualized for individual configurations via the orientational order parameter field. The analysis of positional order confirms the existence of the hexatic phase.

cond-mat.stat-mech↗

arXiv2012

Optimal Fillings - A new spatial subdivision problem related to packing and covering

We present filling as a new type of spatial subdivision problem that is related 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 and the solution space can reduced to the medial axis of a shape. We examine the structure of the solution space in two dimensions. For the filling of polygons, we provide detailed descriptions of a heuristic and a genetic algorithm for finding solutions of maximal discs. We also consider the properties of ideal distributions of N discs in polygons as N approaches infinity.

math.OC↗

arXiv2012

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. For polygons, we provide a heuristic for finding solutions of maximal discs. We consider the properties of ideal distributions of N discs as N approaches infinity. We note an analogy with energy landscapes.

cond-mat.soft↗