Multicentered Gaussian‐based potentials for coarse‐grained polymer simulations: Linking atomistic and mesoscopic scales

Milano, Giuseppe; Goudeau, Sylvain; Müller‐Plathe, Florian

doi:10.1002/polb.20380

Cited by 85 publications

(107 citation statements)

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“…However, the obtained entanglement length is much smaller than the experimental value. Through slight modifications, Spyriouni et al [198] improved the coarse-grained potential functions from previous works [196,197]. The optimized coarse-grained model can capture the correct entanglement length of PS melts [198].…”

Section: (B) (A)mentioning

confidence: 99%

“…Thus, these distribution functions cannot be easily fitted, hindering the derivation of the effective coarse-graining potentials. Milano et al [196] developed and discussed analytical forms for these complex distribution functions. Since these distribution functions always exhibit multiple peaks, they applied multi-centered Gaussian distribution functions to fit them [196]:…”

Section: Smoothing Extrapolation and Convergencementioning

confidence: 99%

“…From these studies, it is important to know that although there are different ways to define the super atom in deriving a coarse-grained model, the static, dynamic or thermodynamic properties of the coarse-grained model should be tested and validated before it is further applied [42]. [195][196][197]; (b) [185,200]; (c) [69]; and (d) [188,201]. Superatom Center…”

Section: (B) (A)mentioning

confidence: 99%

“…Müller-Plathe and his co-workers [195][196][197] adopted the mapping scheme shown in Figure 11a using an IBI method with pressure correction. The developed model successfully reproduces the gyration radius and the Flory characteristic ratio of PS in melts (500 K).…”

Section: (B) (A)mentioning

confidence: 99%

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Challenges in Multiscale Modeling of Polymer Dynamics

et al. 2013

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The mechanical and physical properties of polymeric materials originate from the interplay of phenomena at different spatial and temporal scales. As such, it is necessary to adopt multiscale techniques when modeling polymeric materials in order to account for all important mechanisms. Over the past two decades, a number of different multiscale computational techniques have been developed that can be divided into three categories: (i) coarse-graining methods for generic polymers; (ii) systematic coarse-graining methods and (iii) multiple-scale-bridging methods. In this work, we discuss and compare eleven different multiscale computational techniques falling under these categories and assess them critically according to their ability to provide a rigorous link between polymer chemistry and rheological material properties. For each technique, the fundamental ideas and equations are introduced, and the most important results or predictions are shown and discussed. On the one hand, this review provides a comprehensive tutorial on multiscale computational techniques, which will be of interest to readers newly entering this field; on the other, it presents a critical discussion of the future opportunities and key challenges in the multiscale geometric mapping matrix between all-atomistic and coarse-grained models N number of monomers per chain N e entanglement length, which is the number of monomers between two entanglements n v number of polymer chains per unit volume n ij (n 0 (r ij )) entanglement number (at equilibrium) between particle pairs i and j p r , P R configurational probability distributions in all-atomistic and coarse-grained models, respectively R ee end-to-end distance of polymer chain R G radius of gyration of polymer chain s segment index/contour length variable along primitive chain S(q) single chain coherent dynamic scattering function Z number of entanglements per chain, defined as Z = N/N e α exponent in standard Mittag-Leffler function σ

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Section: (B) (A)mentioning

confidence: 99%

Section: Smoothing Extrapolation and Convergencementioning

confidence: 99%

Section: (B) (A)mentioning

confidence: 99%

Section: (B) (A)mentioning

confidence: 99%

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Challenges in Multiscale Modeling of Polymer Dynamics

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“…7 If the aim of coarse-graining is to reproduce the structure of the higher resolution model, a number of computational methods are available. 8 According to Milano et al the basic idea goes back to Soper,9 which was adapted and named iterative Boltzmann inversion (IBI) by Reith et al 10 IBI reproduces the structure of the underlying atomistic system by means of an iterative optimization scheme through which the effective potentials between superatoms (CG sites) are obtained. 3 It is motivated by Henderson's theorem 11 which states that there is a unique mapping between the radial distribution function (g(r)) and the intermolecular potential (V) for simple pairwise additive and spherically symmetric potentials at a given thermodynamic state point: 12 = − V k T g r ln( ( )) B (1) eq 1 is actually a potential of mean force and it equals the potential energy only in the limit of infinite dilution.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Polystyrene under Confinement: Exploring the Limits of Iterative Boltzmann Inversion

Bayramoglu

Faller

2013

Macromolecules

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ABSTRACT:We explore the limits of a purely structure based coarse-graining technique, the iterative Boltzmann inversion (IBI), in the coarse-graining of a confined concentrated polystyrene solution. In the first place, some technical considerations and challenges encountered in the course of the optimization process are represented. The concepts of the choice of the initial potentials and the cross-dependency of the interactions as well as the order of optimization are discussed in detail. Furthermore, the transferability of a previously developed CG confined polystyrene solution model, the "parent CG confined model", to different degrees of confinement at constant concentration and temperature is examined. We investigate if a CG force field developed for a confined polymer solution by IBI is sensitive to changes in the degree of localization or arrangement of polymers near the surfaces although the concentration is kept constant. For this purpose, reference atomistic simulations on systems of different confinement levels have been performed. The differences in the structure and dynamics of the chains are addressed. Results are compared with those of an unconfined (bulk) system at the same concentration. The chain dimensions and orientations as a function of the distance from the surfaces are also reported. To the best of our knowledge, this is the first computational study that investigates the structural behavior of polymers in close proximity of the surfaces in a concentrated polymer solution rather than in a melt. Transferability of the parent CG confined model is tested by employing the parent force field in CG simulations of the reference systems. Results indicate that the degree of arrangement of monomers and solvent molecules near the surfaces is an important factor that needs to be paid attention to when considering the application of a CG force field developed by IBI to different degrees of confinement.

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How Good Are Coarse‐Grained Polymer Models? A Comparison for Atactic Polystyrene

et al. 2012

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This review provides an overview of the various coarse-grained models that have been developed in the past few years for amorphous polystyrene. Different techniques to develop the force fields and different mapping schemes lead to models that perform differently depending on the properties investigated. This review collects and compares the models to guide the reader in the choice of the best model for the application of interest. It is expected that the central features of the various coarse-graining procedures will also apply to systems other than polystyrene and that some of the conclusions about different coarse-graining strategies are general.

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Multicentered Gaussian‐based potentials for coarse‐grained polymer simulations: Linking atomistic and mesoscopic scales

Cited by 85 publications

References 37 publications

Challenges in Multiscale Modeling of Polymer Dynamics

Challenges in Multiscale Modeling of Polymer Dynamics

Modeling of Polystyrene under Confinement: Exploring the Limits of Iterative Boltzmann Inversion

How Good Are Coarse‐Grained Polymer Models? A Comparison for Atactic Polystyrene

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