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

Abstract

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.

References in corpus (10)

• Strong scaling of general-purpose molecular dynamics simulations on GPUs
• DynamO: A free O(N) general event-driven molecular-dynamics simulator
• Vacancy-stabilized crystalline order in hard cubes
• Event-Driven Molecular Dynamics in Parallel
• Scalable Metropolis Monte Carlo for simulation of hard shapes
• Controlling active self-assembly through broken particle symmetries
• Massively parallel Monte Carlo for many-particle simulations on GPUs
• Solid-phase structures of the Dzugutov pair potential
• Glassy dynamics, spinodal fluctuations, and the kinetic limit of hard-rod nucleation
• Optimal Filling of Shapes

Cited by in corpus (5)

• Hypostatic jammed packings of frictionless nonspherical particles
• Manifestation of dipole-induced disorder in self-assembly of ferroelectric and ferromagnetic nanocubes
• Quasi-static crack propagation with a Griffith criterion using a variational discrete element method
• Energy-Conserving Contact Dynamics of Nonspherical Rigid-Body Particles
• Analyzing Molecular Simulations Trajectories by Utilizing CUDA on GPU Architecture