Exploiting seeding of random number generators for efficient domain decomposition parallelization of dissipative particle dynamics

Afshar, Yaser; Schmid, Friederike; Pishevar, Ahmadreza; Worley, S.

doi:10.1016/j.cpc.2012.12.003

Cited by 24 publications

(30 citation statements)

References 26 publications

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“…1 To accompany the release of this book, we have prepared MATLAB code for many of the algorithms we present. We have found that small systems (harmonic oscillators, double wells, Lennard-Jones trimers, etc.…”

Section: Molecular Dynamics Softwarementioning

confidence: 99%

“…Example 1.2 (Gay-Berne Potential) In simulations of liquid crystals and other systems with a coarse-grained molecular dynamics model, it is common to replace groups of atoms by ellipsoidal rigid bodies. O u 1 and O u 2 are unit vectors representing the orientations of the molecules. 1.11a.…”

Section: Example 11 (N-alkane Chains)mentioning

confidence: 99%

“…A common choice for this purpose is the Gay-Berne potential [143] (and various generalizations) which takes into consideration the separation between ellipsoids as well as their orientations relative to the vector linking their centers of mass. (b) The Gay-Berne potential models the anisotropic interaction between nonspherical coarse-grained molecules; potentials are described by the orientation of the molecules with respect to a fixed frame and the separation between their centers of mass 1 1.11b, where the centers of mass of two molecules are q 1 and q 2 , q 12 D q 2 q 1 being the separation vector.…”

Section: Example 11 (N-alkane Chains)mentioning

confidence: 99%

“…1.3). 1 The capsid assembly consists of over a thousand proteins, each of which contains thousands of atoms. The proteins are arranged across the cone-shaped surface to form hexamers and pentamers, in much the same way that a buckminsterfullerene includes both pentagonal and hexagonal faces.…”

Section: Introductionmentioning

confidence: 99%

“…for f D f 1 where˛D g=`> 0, where g is the gravitational constant and`is the length. 3 that a splitting method defines a mapping via a composition of the flow maps associated to additive decomposition of a vector field, i.e.…”

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Problems in engineering, computational science, and the physical and biological sciences are using increasingly sophisticated mathematical techniques. Thus, the bridge between the mathematical sciences and other disciplines is heavily traveled. The correspondingly increased dialog between the disciplines has led to the establishment of the series: Interdisciplinary Applied Mathematics.The purpose of this series is to meet the current and future needs for the interaction between various science and technology areas on the one hand and mathematics on the other. This is done, firstly, by encouraging the ways that mathematics may be applied in traditional areas, as well as point towards new and innovative areas of applications; and, secondly, by encouraging other scientific disciplines to engage in a dialog with mathematicians outlining their problems to both access new methods and suggest innovative developments within mathematics itself.The series will consist of monographs and high-level texts from researchersworking on the interplay between mathematics and other fields of science and technology.More information about this series at

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Section: Molecular Dynamics Softwarementioning

confidence: 99%

Section: Example 11 (N-alkane Chains)mentioning

confidence: 99%

Section: Example 11 (N-alkane Chains)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Stochastic Differential Equations for Practical Simulation of Gene Circuits

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Extended Variable Methods

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In the previous chapter we described numerical methods for solving the equations of Langevin dynamics, a system of stochastic differential equations that sample the canonical ensemble. The methods allow us to compute averages of the formwhere ˇD exp. ˇH/, and H is the system Hamiltonian. In this chapter we extend the range of systems that can be used to accomplish this task using methods based on the incorporation of auxiliary variables. We also consider schemes for sampling alternative thermodynamic ensembles, for example those defined by constant number of atoms N, pressure P and temperature T. In some of the methods, based on purely deterministic models, the ergodic property may be compromised, so an important issue is to design schemes that restore this. We will focus on methods that introduce random processes in direct contact with the auxiliary variables. Besides canonical or configurational sampling, another common purpose of molecular simulation is the calculation of a dynamical (time-dependent) quantity such as the rearrangement of a molecular group over time or a diffusion rate. The only dynamical calculations that make sense in the context of molecular modelling, where trajectories are chaotic, are those that involve some sort of average. We consider this average in a thermodynamic sense and assume that the calculation can be formulated as the determination of the canonically weighted correlation between two quantities evaluated at different times, for example

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Exploiting seeding of random number generators for efficient domain decomposition parallelization of dissipative particle dynamics

Cited by 24 publications

References 26 publications

Molecular Dynamics

Molecular Dynamics

Stochastic Differential Equations for Practical Simulation of Gene Circuits

Extended Variable Methods

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