Equation of motion for coarse-grained simulation based on microscopic description

Kinjo, Tomoyuki; Hyodo, Shi‐aki

doi:10.1103/physreve.75.051109

Cited by 126 publications

(136 citation statements)

References 26 publications

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“…Reading the fluctuating force term as a random force term, we can see that the resultant equation of motion corresponds to a generalised Langevin equation, of which analyses have been mostly restricted to the system having periodic external forces [11]. After a brief introduction, we make several remarks in a more general sense rather than the point of view adopted in the previous contributions [5,6]. The discussion will be related to a generalised Langevin equation, the comparison of the determination procedure of the mean force with the conventional CG method, the role of friction and fluctuating force terms in conventional coarse-graining simulations, an extension to coarse-graining for arbitrary degrees of freedom (DOFs) and coarse-graining dynamics described by continuous distribution functions as another candidate.…”

Section: Introductionmentioning

confidence: 97%

“…By analysing the fluctuating force term, we also derived correspondences of the present CG equation of motion to conventional CG particle dynamics methods, i.e. Brownian dynamics (BD) and dissipative particle dynamics (DPD), [5]. The difference in these simulation methods was found to appear in a characteristic of the model of the random force on an identical basis.…”

Section: Introductionmentioning

confidence: 99%

“…Details of the derivation are given in reference [5]. The idea of coarse graining adopted here is to divide the total N t molecules into N groups (or clusters) which consist of n a ða ¼ 1; .…”

Section: Outline For Derivation Of the Present Cg Equation Of Motionmentioning

confidence: 99%

“…To derive the equation of motion for the CG particles, we introduced the projection operator [4] and applied to divide the Liouville equation for the phase space density for CG particles. In the recent publication by the present author's group [5], the derived expression was found to be constructed from the mean force, the friction force and the microscopic fluctuating force terms. In the next section, the explicit expression is given as Equation (8), with a brief explanation of its derivation.…”

Section: Introductionmentioning

confidence: 99%

“…Some comments on the characteristics of the resultant CG equation of motion can be found in reference [5]. They were rather restricted within that related to showing correspondences to the conventional coarse-graining simulation methods.…”

Section: Introductionmentioning

confidence: 99%

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Analyses of coarse graining procedure for mesoscale simulation

Hyodo

2012

Molecular Simulation

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In order to meet great progress in multi-scale modelling, one of the important issues is to know about detailed process of coarse graining. The coarse graining is a standard technique of statistical mechanics to extract specific degree(s) of freedom from a many component system. Many kinds of coarse-graining simulation methods have already been proposed. Because most of them were constructed based on a priori assumptions, making detailed analyses has been difficult from the microscopic points of view. Recently, a generalised coarse-grained (CG) equation of motion has been reported for many particle centre of mass systems straightforwardly derived from the explicit information about all the degrees of freedom at the microscopic level. This equation is mostly exact for the dynamics of CG particles defined in classical many particle systems. Therefore, making discussions about characteristics of this CG equation of motion is useful for obtaining detailed knowledge on the coarse graining from the microscopic points of view. In the present article, a review on recent discussions about characteristics of this generalised CG equation of motion is given. Previous conventional coarse-graining particle dynamics simulations can be categorised as some special cases. Generalisation, confirmation and progress of the fundamental knowledge of coarse graining are partly realised from the present discussions.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Outline For Derivation Of the Present Cg Equation Of Motionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analyses of coarse graining procedure for mesoscale simulation

Hyodo

2012

Molecular Simulation

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Dissipative Particle Dynamics

Pivkin

Caswell

Karniadakisa

2010

Reviews in Computational Chemistry

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Dissipative Particle Dynamics (DPD) is a mesoscopic simulation method, potentially very effective in simulating mesoscale hydrodynamics and soft matter. This thesis addresses open theoretical and algorithmic questions of DPD and demonstrates the new developments with applications to blood flow in health as well as in sickle cell anemia. The first part investigates the intrinsic relation of DPD to the microscopic Molecular Dynamics (MD) method through the Mori-Zwanzig theory. We provide a physical explanation for the dissipative and random forces by constructing a mesoscopic system directly from a microscopic one. The relationship between DPD and MD is quantified and the many-body effect on the hydrodynamics of the coarsegrained system is discussed. We then address algorithmic issues and develop a simple approach for imposing proper no-slip boundary conditions for wall-bounded fluid systems and outflow boundary conditions for open fluid systems. The second part deals with blood flow applications. First, we use DPD and multi-scale red blood cell models to investigate the transition of blood flow from Newtonian to non-Newtonian behavior as the arteriole size decreases. Then, we develop a multi-scale model for the sickle red blood cells (RBCs), accounting for diversity in shapes and polymerization of hemoglobin. Subsequently, we use this model to investigate abnormal rheology and hemodynamics of the sickle blood flow under different physiological conditions.Despite the increased flow resistance, no occlusion was observed in a straight tube under any conditions unless an adhesive dynamics model was explicitly incorporated into our simulations. This new adhesion model includes both sickle RBCs as well as leukocytes. The former interact with the vascular endothelium, with the deformable sickle cells (SS2) exhibiting larger adhesion. The adherent SS2 cells further trap rigid irreversible sickle cells (SS4) resulting in vaso-occlusion in vessels less than 15µm. Under inflammation, adherent leukocytes may also trap SS4 cells resulting in vaso-occlusion in even larger vessels.

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Optimization of parallel implementation ofUNRESpackage for coarse‐grained simulations to treat large proteins

et al. 2022

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We report major algorithmic improvements of the UNRES package for physics-based coarse-grained simulations of proteins. These include (i) introduction of interaction lists to optimize computations, (ii) transforming the inertia matrix to a pentadiagonal form to reduce computing and memory requirements, (iii) removing explicit angles and dihedral angles from energy expressions and recoding the most time-consuming energy/force terms to minimize the number of operations and to improve numerical stability, (iv) using OpenMP to parallelize those sections of the code for which distributed-memory parallelization involves unfavorable computing/communication time ratio, and (v) careful memory management to minimize simultaneous access of distant memory sections. The new code enables us to run molecular dynamics simulations of protein systems with size exceeding 100,000 amino-acid residues, reaching over 1 ns/day (1 μs/day in all-atom timescale) with 24 cores for proteins of this size.

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Equation of motion for coarse-grained simulation based on microscopic description

Cited by 126 publications

References 26 publications

Analyses of coarse graining procedure for mesoscale simulation

Analyses of coarse graining procedure for mesoscale simulation

Dissipative Particle Dynamics

Optimization of parallel implementation ofUNRESpackage for coarse‐grained simulations to treat large proteins

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