Transport properties controlled by a thermostat: An extended dissipative particle dynamics thermostat

Junghans, Christoph; Praprotnik, Matej; Kremer, Kurt

doi:10.1039/b713568h

Cited by 127 publications

(128 citation statements)

References 55 publications

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“…193 that extends DPD-VV to allow DPD simulation to be performed in both constant pressure and constant surface tension, which has allowed for the simulation of biological membranes using DPD. 194 Thermostating also remains an active field, and the latest addition is the robust ''extended DPD thermostat'' by Junghans et al 195 4 Analytical approaches: regarding a systematic derivation of DPD. The standard DPD with soft potentials as presented above is phenomenological.…”

Section: Dissipative Particle Dynamics (Dpd)mentioning

confidence: 99%

Multiscale modeling of emergent materials: biological and soft matter

Murtola

Bunker

Vattulainen

et al. 2009

Phys. Chem. Chem. Phys.

260

234

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On using a too large integration time step in molecular dynamics simulations of coarse-grained molecular models Moritz Winger, Daniel Trzesniak, Riccardo Baron and Wilfred F. In this review, we focus on four current related issues in multiscale modeling of soft and biological matter. First, we discuss how to use structural information from detailed models (or experiments) to construct coarse-grained ones in a hierarchical and systematic way. This is discussed in the context of the so-called Henderson theorem and the inverse Monte Carlo method of Lyubartsev and Laaksonen. In the second part, we take a different look at coarse graining by analyzing conformations of molecules. This is done by the application of self-organizing maps, i.e., a neural network type approach. Such an approach can be used to guide the selection of the relevant degrees of freedom. Then, we discuss technical issues related to the popular dissipative particle dynamics (DPD) method. Importantly, the potentials derived using the inverse Monte Carlo method can be used together with the DPD thermostat. In the final part we focus on solvent-free modeling which offers a different route to coarse graining by integrating out the degrees of freedom associated with solvent.

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Section: Dissipative Particle Dynamics (Dpd)mentioning

confidence: 99%

Multiscale modeling of emergent materials: biological and soft matter

Murtola

Bunker

Vattulainen

et al. 2009

Phys. Chem. Chem. Phys.

260

234

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“…DPD can be extended to thermalize the perpendicular component of the interparticle velocity as well, thereby allowing more control over the transport properties of the model [49,57].…”

Section: Dissipative Particle Dynamics (Dpd)mentioning

confidence: 99%

Lattice Boltzmann Simulations of Soft Matter Systems

Dünweg¹,

Ladd²

2008

Advances in Polymer Science

201

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This article concerns numerical simulations of the dynamics of particles immersed in a continuum solvent. As prototypical systems, we consider colloidal dispersions of spherical particles and solutions of uncharged polymers. After a brief explanation of the concept of hydrodynamic interactions, we give a general overview of the various simulation methods that have been developed to cope with the resulting computational problems. We then focus on the approach we have developed, which couples a system of particles to a lattice-Boltzmann model representing the solvent degrees of freedom. The standard D3Q19 lattice-Boltzmann model is derived and explained in depth, followed by a detailed discussion of complementary methods for the coupling of solvent and solute. Colloidal dispersions are best described in terms of extended particles with appropriate boundary conditions at the surfaces, while particles with internal degrees of freedom are easier to simulate as an arrangement of mass points with frictional coupling to the solvent. In both cases, particular care has been taken to simulate thermal fluctuations in a consistent way. The usefulness of this methodology is illustrated by studies from our own research, where the dynamics of colloidal and polymeric systems has been investigated in both equilibrium and nonequilibrium situations.

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“…For example, while the fluid viscosity is directly accessible in LB methods, it becomes a combination of different parameters and can be controlled only indirectly in DPD [88,89]. Similarly, while DPD and LB can only approximate the continuous-time dynamics of the fluid when the discrete time step is small, SRD is proven to yield correct long-time hydrodynamics for any step size.…”

Section: Comparison Of Fluid Modelsmentioning

confidence: 99%

Modeling the separation of macromolecules: A review of current computer simulation methods

et al. 2009

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Theory and numerical simulations play a major role in the development of improved and novel separation methods. In some cases, computer simulations predict counterintuitive effects that must be taken into account in order to properly optimize a device. In other cases, simulations allow the scientist to focus on a subset of important system parameters. Occasionally, simulations even generate entirely new separation ideas! In this article, we review the main simulation methods that are currently being used to model separation techniques of interest to the readers of Electrophoresis. In the first part of the article, we provide a brief description of the numerical models themselves, starting with molecular methods and then moving towards more efficient coarse-grained approaches. In the second part, we briefly examine nine separation problems and some of the methods used to model them. We conclude with a short discussion of some notoriously hard-to-model separation problems and a description of some of the available simulation software packages.

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Transport properties controlled by a thermostat: An extended dissipative particle dynamics thermostat

Cited by 127 publications

References 55 publications

Multiscale modeling of emergent materials: biological and soft matter

Multiscale modeling of emergent materials: biological and soft matter

Lattice Boltzmann Simulations of Soft Matter Systems

Modeling the separation of macromolecules: A review of current computer simulation methods

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