On the Density Dependence of the Integral Equation Coarse-Graining Effective Potential

2017 ◽  
Vol 122 (13) ◽  
pp. 3426-3440 ◽  
Author(s):  
Mohammadhasan Dinpajooh ◽  
Marina G. Guenza
Keyword(s):  
Integral Equation ◽  
Density Dependence ◽  
Effective Potential ◽  
Coarse Graining

Integral equation theory based direct and accelerated systematic coarse-graining approaches

2018 ◽  
Vol 148 (21) ◽  
pp. 214105 ◽  
Author(s):  
S. Y. Mashayak ◽  
Linling Miao ◽  
N. R. Aluru
Keyword(s):  
Integral Equation ◽  
Coarse Graining ◽  
Integral Equation Theory

scholarly journals Effective interactions of knotted ring polymers

2013 ◽  
Vol 41 (2) ◽  
pp. 630-634 ◽  
Author(s):  
Arturo Narros ◽  
Angel J. Moreno ◽  
Christos N. Likos
Keyword(s):  
Present Article ◽  
Effective Potential ◽  
Coarse Graining ◽  
The Self ◽  
Relative Importance ◽  
Effective Interactions ◽  
Polymeric Systems ◽  
Ring Polymers

In the present article, we review recent computational investigations on the properties of ring polymers in solution. In particular, we focus on effective interactions obtained by means of coarse-graining techniques. We discuss the relative importance of the self-avoidance and the topological contributions in the qualitative features of the effective potential. We extend our previous results on identical rings and determine the effective potential between dissimilar ring polymers of distinct topology and size. The results obtained reveal the dramatic effects of the specific topology on the effective interactions, and hence in the structural correlations, of polymeric systems.

Coarse-graining simulation approaches for polymer melts: the effect of potential range on computational efficiency

Soft Matter ◽  
2018 ◽  
Vol 14 (35) ◽  
pp. 7126-7144 ◽  
Author(s):  
Mohammadhasan Dinpajooh ◽  
Marina G. Guenza
Keyword(s):  
Integral Equation ◽  
Computational Efficiency ◽  
Polymer Melts ◽  
Coarse Graining ◽  
Atomistic Simulations ◽  
Potential Range ◽  
Variable Resolution ◽  
Soft Spheres ◽  
High Level

The integral equation coarse-graining (IECG) approach is a promising high-level coarse-graining (CG) method for polymer melts, with variable resolution from soft spheres to multi CG sites, which preserves the structural and thermodynamical consistencies with the related atomistic simulations. Taking advantage of the accuracy and transferability of the IECG model, we investigate the relation between the level of coarse-graining, the range of the CG potential, and the computational efficiency of a CG model.

Iterative integral equation methods for structural coarse-graining

2021 ◽  
Vol 154 (8) ◽  
pp. 084118
Author(s):  
Marvin P. Bernhardt ◽  
Martin Hanke ◽  
Nico F. A. van der Vegt
Keyword(s):  
Integral Equation ◽  
Coarse Graining ◽  
Integral Equation Methods

Coarse-graining in suspensions of charged nanoparticles

2008 ◽  
Vol 80 (6) ◽  
pp. 1229-1238 ◽  
Author(s):  
Vincent Dahirel ◽  
Marie Jardat ◽  
Jean-François Dufrêche ◽  
Ivan Lucas ◽  
Serge Durand-Vidal ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Effective Potential ◽  
Brownian Dynamics ◽  
Effective Diffusion ◽  
Coarse Graining ◽  
Coarse Grain ◽  
Salt Ions ◽  
Charged Nanoparticles ◽  
Dynamical Properties ◽  
Added Salt

A coarse-grain description of nanocolloidal suspensions in the presence of an added salt is presented here. It enables us to simulate trajectories of the nanoparticles from effective functions that depend on average densities of salt ions. In practice, the ion-averaged effective potential is used as input of a Brownian dynamics (BD) simulation. This potential may be derived by various methods, ranging from purely analytical to fully numerical ones. For the description of dynamical properties, this simulation also requires an effective diffusion coefficient that must be calculated or experimentally determined, and that accounts for the effects of microions on the mobility of the nanoparticles. The different versions of our coarse-graining procedure are applied to the case of a maghemite suspension, for which an explicit description of all ions would be very time-consuming.

scholarly journals Effective potential reveals evolutionary trajectories in complex fitness landscapes

2019 ◽  
Author(s):  
Matteo Smerlak
Keyword(s):  
Effective Potential ◽  
Evolutionary Dynamics ◽  
Fitness Landscape ◽  
Coarse Graining ◽  
Fitness Landscapes ◽  
Coarse Grained ◽  
Mutational Robustness ◽  
Sequencing Technologies ◽  
Wide Range ◽  
Evolutionary Trajectories

AbstractGrowing efforts to measure fitness landscapes in molecular and microbial systems are premised on a tight relationship between landscape topography and evolutionary trajectories. This relationship, however, is far from being straightforward: depending on their mutation rate, Darwinian populations can climb the closest fitness peak (survival of the fittest), settle in lower regions with higher mutational robustness (survival of the flattest), or fail to adapt altogether (error catastrophes). These bifurcations highlight that evolution does not necessarily drive populations “from lower peak to higher peak”, as Wright imagined. The problem therefore remains: how exactly does a complex landscape topography constrain evolution, and can we predict where it will go next? Here I introduce a generalization of quasispecies theory which identifies metastable evolutionary states as minima of an effective potential. From this representation I derive a coarse-grained, Markov state model of evolution, which in turn forms a basis for evolutionary predictions across a wide range of mutation rates. Because the effective potential is related to the ground state of a quantum Hamiltonian, my approach could stimulate fruitful interactions between evolutionary dynamics and quantum many-body theory.SIGNIFICANCE STATEMENTThe course of evolution is determined by the relationship between heritable types and their adaptive values, the fitness landscape. Thanks to the explosive development of sequencing technologies, fitness landscapes have now been measured in a diversity of systems from molecules to micro-organisms. How can we turn these data into evolutionary predictions? I show that preferred evolutionary trajectories are revealed when the effects of selection and mutations are blended in a single effective evolutionary force. With this reformulation, the dynamics of selection and mutation becomes Markovian, bringing a wealth of classical visualization and analysis tools to bear on evolutionary dynamics. Among these is a coarse-graining of evolutionary dynamics along its metastable states which greatly reduces the complexity of the prediction problem.

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