Multipolar Expansion Methods for Coarse Grained Force Fields [PowerPoint Submission]

2006 ◽  
Vol 978 ◽  
Author(s):  
Sheng D. Chao
Keyword(s):  
Molecular Dynamics ◽  
Quantum Chemistry ◽  
Large Scale ◽  
Force Fields ◽  
Coarse Graining ◽  
Expansion Method ◽  
Distribution Functions ◽  
Coarse Grained ◽  
Quantum Chemistry Calculations ◽  
Multipolar Expansion

AbstractCurrent large scale atomistic simulations remain too computationally demanding to be generally applicable to industrial and bioengineering materials. It is desirable to develop multiscale modeling algorithms to perform efficient and informative mesoscopic simulations. Here we present a multipolar expansion method to construct coarse grained force fields (CGFF) for polymer nanostructures and nanocomposites. This model can effectively capture the stereochemical response to anisotropic long-range interactions and can be systematically improved upon adding higher order terms. The coarse-graining procedure forms the basis to perform a hierarchy of multiscale simulations starting with the quantum chemistry calculations to coarse grained molecular dynamics, hopefully toward continuum modeling. We have applied this procedure to molecular clusters such as alkane, benzene, and fullerene. For liquid alkane, molecular dynamics simulations using the CGFF can reproduce the pair distribution functions using atomistic force fields. Molecular mechanics simulations using the CGFF can well reproduce the energetics of benzene clusters from quantum chemistry electronic structure calculations. Subtle anisotropy in the interaction potentials of the fullerene dimer using the Brenner force field can also be well represented by the model. It is promising this procedure can be standardized and further extended.

scholarly journals Dissipative particle dynamics model of homogalacturonan based on molecular dynamics simulations

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
P. M. Pieczywek ◽  
W. Płaziński ◽  
A. Zdunek
Keyword(s):  
Molecular Dynamics ◽  
Force Field ◽  
Large Scale ◽  
Dissipative Particle Dynamics ◽  
Three Dimensional ◽  
Structural Features ◽  
Particle Dynamics ◽  
Distribution Functions ◽  
Coarse Grained ◽  
Dynamics Model

Abstract In this study we present an alternative dissipative particle dynamics (DPD) parametrization strategy based on data extracted from the united-atom molecular simulations. The model of the homogalacturonan was designed to test the ability of the formation of large-scale structures via hydrogen bonding in water. The extraction of coarse-grained parameters from atomistic molecular dynamics was achieved by means of the proposed molecule aggregation algorithm based on an iterative nearest neighbour search. A novel approach to a time-scale calibration scheme based on matching the average velocities of coarse-grained particles enabled the DPD forcefield to reproduce essential structural features of homogalacturonan molecular chains. The successful application of the proposed parametrization method allowed for the reproduction of the shapes of radial distribution functions, particle velocities and diffusivity of the atomistic molecular dynamics model using DPD force field. The structure of polygalacturonic acid molecules was mapped into the DPD force field by means of the distance and angular bond characteristics, which closely matched the MD results. The resulting DPD trajectories showed that randomly dispersed homogalacturonan chains had a tendency to aggregate into highly organized 3D structures. The final structure resembled a three-dimensional network created by tightly associated homogalacturonan chains organized into thick fibres.

scholarly journals Renormalization group theory of molecular dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daiji Ichishima ◽  
Yuya Matsumura
Keyword(s):  
Molecular Dynamics ◽  
Renormalization Group ◽  
Critical Exponent ◽  
Computational Efficiency ◽  
Large Scale ◽  
Dissipative Particle Dynamics ◽  
Computational Cost ◽  
Coarse Graining ◽  
Spatial Dimension ◽  
Deborah Number

AbstractLarge scale computation by molecular dynamics (MD) method is often challenging or even impractical due to its computational cost, in spite of its wide applications in a variety of fields. Although the recent advancement in parallel computing and introduction of coarse-graining methods have enabled large scale calculations, macroscopic analyses are still not realizable. Here, we present renormalized molecular dynamics (RMD), a renormalization group of MD in thermal equilibrium derived by using the Migdal–Kadanoff approximation. The RMD method improves the computational efficiency drastically while retaining the advantage of MD. The computational efficiency is improved by a factor of $$2^{n(D+1)}$$ 2 n ( D + 1 ) over conventional MD where D is the spatial dimension and n is the number of applied renormalization transforms. We verify RMD by conducting two simulations; melting of an aluminum slab and collision of aluminum spheres. Both problems show that the expectation values of physical quantities are in good agreement after the renormalization, whereas the consumption time is reduced as expected. To observe behavior of RMD near the critical point, the critical exponent of the Lennard-Jones potential is extracted by calculating specific heat on the mesoscale. The critical exponent is obtained as $$\nu =0.63\pm 0.01$$ ν = 0.63 ± 0.01 . In addition, the renormalization group of dissipative particle dynamics (DPD) is derived. Renormalized DPD is equivalent to RMD in isothermal systems under the condition such that Deborah number $$De\ll 1$$ D e ≪ 1 .

A new spectral coarse-graining algorithm based on K-means clustering in complex networks

2019 ◽  
Vol 33 (01) ◽  
pp. 1850421 ◽  
Author(s):  
Lang Zeng ◽  
Zhen Jia ◽  
Yingying Wang
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Dynamic Properties ◽  
Coarse Graining ◽  
Kuramoto Model ◽  
Coarse Grained ◽  
Original Network ◽  
Topological Information ◽  
Real World Applications ◽  
Large Scale Networks

Coarse-graining of complex networks is one of the important algorithms to study large-scale networks, which is committed to reducing the size of networks while preserving some topological information or dynamic properties of the original networks. Spectral coarse-graining (SCG) is one of the typical coarse-graining algorithms, which can keep the synchronization ability of the original network well. However, the calculation of SCG is large, which limits its real-world applications. And it is difficult to accurately control the scale of the coarse-grained network. In this paper, a new SCG algorithm based on K-means clustering (KCSCG) is proposed, which cannot only reduce the amount of calculation, but also accurately control the size of coarse-grained network. At the same time, KCSCG algorithm has better effect in keeping the network synchronization ability than SCG algorithm. A large number of numerical simulations and Kuramoto-model example on several typical networks verify the feasibility and effectiveness of the proposed algorithm.

scholarly journals Antimicrobial action of the cationic peptide, chrysophsin-3: a coarse-grained molecular dynamics study

Soft Matter ◽  
2018 ◽  
Vol 14 (15) ◽  
pp. 2796-2807 ◽  
Author(s):  
Andrea Catte ◽  
Mark R. Wilson ◽  
Martin Walker ◽  
Vasily S. Oganesyan
Keyword(s):  
Molecular Dynamics ◽  
Large Scale ◽  
Md Simulations ◽  
Antimicrobial Action ◽  
Coarse Grained ◽  
Cationic Peptide ◽  
Coarse Grained Molecular Dynamics

Antimicrobial action of a cationic peptide is modelled by large scale MD simulations.

scholarly journals Exploring the landscape of model representations

2020 ◽  
Vol 117 (39) ◽  
pp. 24061-24068 ◽  
Author(s):  
Thomas T. Foley ◽  
Katherine M. Kidder ◽  
M. Scott Shell ◽  
W. G. Noid
Keyword(s):  
Statistical Physics ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Coarse Graining ◽  
Order Parameters ◽  
Microscopic Model ◽  
Coarse Grained ◽  
Proper Order ◽  
Complex Phenomena ◽  
Protein Fluctuations

The success of any physical model critically depends upon adopting an appropriate representation for the phenomenon of interest. Unfortunately, it remains generally challenging to identify the essential degrees of freedom or, equivalently, the proper order parameters for describing complex phenomena. Here we develop a statistical physics framework for exploring and quantitatively characterizing the space of order parameters for representing physical systems. Specifically, we examine the space of low-resolution representations that correspond to particle-based coarse-grained (CG) models for a simple microscopic model of protein fluctuations. We employ Monte Carlo (MC) methods to sample this space and determine the density of states for CG representations as a function of their ability to preserve the configurational information, I, and large-scale fluctuations, Q, of the microscopic model. These two metrics are uncorrelated in high-resolution representations but become anticorrelated at lower resolutions. Moreover, our MC simulations suggest an emergent length scale for coarse-graining proteins, as well as a qualitative distinction between good and bad representations of proteins. Finally, we relate our work to recent approaches for clustering graphs and detecting communities in networks.

Designing an ordered template of cylindrical arrays based on a simple flat plate confinement of block copolymers: a coarse-grained molecular dynamics study

Soft Matter ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 597-613 ◽  
Author(s):  
Shubham Pinge ◽  
Guanyang Lin ◽  
Durairaj Baskaran ◽  
Munirathna Padmanaban ◽  
Yong Lak Joo
Keyword(s):  
Molecular Dynamics ◽  
Block Copolymers ◽  
Flat Plate ◽  
Large Scale ◽  
Coarse Grained ◽  
Coarse Grained Molecular Dynamics

In this paper we study the morphology formed by asymmetric di-block copolymers (di-BCPs) under various confinements using a large-scale coarse-grained molecular dynamics (CGMD) framework.

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