Dissipative particle dynamics: Bridging the gap between atomistic and mesoscopic simulation

Groot, Robert D.; Warren, Patrick B.

doi:10.1063/1.474784

Cited by 4,030 publications

(6,001 citation statements)

References 23 publications

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“…In addition, their numerical solution to the equations of motion made use of an Euler algorithm which violates time reversibility. This was corrected by Espan˜ol and Warren 137,138 and Warren, who in collaboration with Groot, went on to present the first workable numerical algorithm 129 to solve the DPD equations of motion.…”

Section: Dissipative Particle Dynamics (Dpd)mentioning

confidence: 99%

“…The first workable algorithm to solve the DPD equations of motion was developed by Groot and Warren. 129 The algorithm (GW) is a modification of the VV algorithm, where all the forces, like in VV, are only modified once per timestep, however the dissipative forces are modified based on intermediate ''predicted'' velocities, evaluated at a timestep determined by a phenomenological parameter l. The GW algorithm consists of the following steps:…”

Section: Dissipative Particle Dynamics (Dpd)mentioning

confidence: 99%

See 1 more Smart Citation

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%

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

View full text Add to dashboard Cite

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“…22 Nonetheless, the computing cost of these explicit solvent methods tends to become prohibitively high for systems of very large number of solvent particles, primarily because of the need in computing pair-wise interactions among solvent particles by these methods. 23,24 Therefore, it is desirable to explore other approaches capable of significantly reducing the cost for computing solvent-solvent interactions while maintaining sufficient level of simulation accuracy. One possible approach is the hybrid MD method that models intra-polymer and polymer-solvent interactions by MD and solvent-solvent interactions by simplified algorithms, which has been used in three forms.…”

Section: Introductionmentioning

confidence: 99%

“…25 The second combines MD with lattice Boltzmann for studying electrophoretic properties of highly charged colloids, [26][27][28] and the third combines MD with mesoscale treatment of solvent-solvent interactions for studying solvent effect on polymer dynamics. 20,[29][30][31] By combining the advantages of the coarsegained treatment in CG-MD 17,18 and DPD 22 with the efficient modeling of solvent-solvent interactions of the hybrid MD approach, 23,24,[26][27][28] we introduced and tested a new coarsegrained hybrid MD (CGH-MD) approach as a potentially useful method for complementing the more rigorous methods to simulate systems of larger number of solvent particles without substantially losing simulation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Simulation of DNA electrophoresis in systems of large number of solvent particles by coarse‐grained hybrid molecular dynamics approach

Wang

Liu

et al. 2008

J Comput Chem

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Simulation of DNA electrophoresis facilitates the design of DNA separation devices. Various methods have been explored for simulating DNA electrophoresis and other processes using implicit and explicit solvent models. Explicit solvent models are highly desired but their applications may be limited by high computing cost in simulating large number of solvent particles. In this work, a coarse-grained hybrid molecular dynamics (CGH-MD) approach was introduced for simulating DNA electrophoresis in explicit solvent of large number of solvent particles. CGH-MD was tested in the simulation of a polymer solution and computation of nonuniform charge distribution in a cylindrical nanotube, which shows good agreement with observations and those of more rigorous computational methods at a significantly lower computing cost than other explicit-solvent methods. CGH-MD was further applied to the simulation of DNA electrophoresis in a polymer solution and in a well-studied nanofluidic device. Simulation results are consistent with observations and reported simulation results, suggesting that CGH-MD is potentially useful for studying electrophoresis of macromolecules and assemblies in nanofluidic, microfluidic, and microstructure array systems that involve extremely large number of solvent particles, nonuniformly distributed electrostatic interactions, bound and sequestered water molecules.

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“…For f ij , the short-range interaction forces of dissipative particle dynamics (DPD) are used. Working with reduced units and using the standard parameter values [19] for a passive DPD fluid, we set k B T = 1.0, r c = 1.0, A = 25.0, γ = 4.5, and ρ 2D = N L 2 = 2.5 (two-dimensional (2D) analog of ρ 3D = 4.0), where L is the dimensionless edge length of the square computational domain. Apart from standard DPD forces, we incorporate selfpropulsion through a flocking term…”

mentioning

confidence: 99%

Motility versus fluctuations in mixtures of self-motile and passive agents

Hinz¹,

Panchenko

Kim

et al. 2014

Soft Matter

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Many biological systems consist of self-motile and passive agents both of which contribute to overall functionality. However, there are very few studies of the properties of such mixtures. Here we formulate a model for mixtures of self-motile and passive agents and show that the model gives rise to three different dynamical phases: a disordered mesoturbulent phase, a polar flocking phase, and a vortical phase characterized by large-scale counterrotating vortices. We use numerical simulations to construct a phase diagram and compare the statistical properties of the different phases of the model with self-motile bacterial suspensions. Our findings afford specific insights regarding the interaction of microorganisms and passive particles and provide novel strategic guidance for efficient technological realizations of artificial active matter. The self-motility of individual agents leads to remarkable features of bacterial suspensions, in-vitro networks of protein filaments, and the cytoskeletons of living cells. Likewise, macroscopic active systems, such as animal colonies exhibit swarming, herding, and flocking behaviors that appear to share phenomenological similarities with their microscopic counterparts. Such phenomena include polar ordering, large-scale correlated motion, and intriguing rheological properties [1]. However, biological systems often consist of multiple species which differ in their motilities and other attributes. For example, the emergence of different phenotypes in microbial biofilms generates heterogeneous populations of bacteria [2][3][4][5][6]. In biological systems such as biofilms individual organisms die, malfunction, or lose their flagella, thereby becoming partially or completely immotile. Despite the ubiquity of systems with heterogeneous motility properties, such mixtures have received little attention. Apart from the work of McCandlish et al. [7], who report spontaneous segregation in simulations of self-motile and passive rodshaped agents, we are unaware of any studies of the properties of mixtures of self-motile and passive agents.Yet, insights regarding the salient biological and mechanical interactions are of great relevance to understanding biological systems and might enable progress in potential technological applications including, in particular, the design of artificial active matter systems. For example, techniques of synthetic biology and systems biology [8-13] have made it possible to fabricate bacterial strains with engineered gene-regulation circuits that produce predefined spatial and temporal patterns. Similarly, artificial self-motile agents can be realized through catalytically driven Janus particles [14][15][16][17][18]. From a technological perspective, it is of key importance to know whether it is possible to use a small number of these potentially difficult to manufacture agents to drive other passive agents and thereby generate desirable flow patterns. Having an understanding of how many active agents are required for such a principle seems particularly cruci...

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Dissipative particle dynamics: Bridging the gap between atomistic and mesoscopic simulation

Cited by 4,030 publications

References 23 publications

Multiscale modeling of emergent materials: biological and soft matter

Multiscale modeling of emergent materials: biological and soft matter

Simulation of DNA electrophoresis in systems of large number of solvent particles by coarse‐grained hybrid molecular dynamics approach

Motility versus fluctuations in mixtures of self-motile and passive agents

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