A coarse-grained molecular dynamics – reactive Monte Carlo approach to simulate hyperbranched polycondensation

Zhang, Zidan; Wang, Long; Wang, Zilu; He, Xuehao; Chen, Yu; Müller‐Plathe, Florian; Böhm, Michael

doi:10.1039/c4ra10271a

Cited by 13 publications

(20 citation statements)

References 97 publications

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“…Genzer et al, [4,5] He and the coworkers, [6,7] Berezkin et al, [8][9][10][11] Lu et al, [12] Yong et al, [13,14] Jang, [15] and Mukherji et al, [16] including Rabbel et al [17] in their recent works, all adopted similar reaction probability-based protocols to establish their respective reaction models. The simulation studies based on this idea can be identified as the second class, that is, "probability-based" protocols.…”

Section: Introductionmentioning

confidence: 99%

A kinetic chain growth algorithm in coarse-grained simulations

Zhu

et al. 2016

J. Comput. Chem.

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We propose a kinetic chain growth algorithm for coarse-grained (CG) simulations in this work. By defining the reaction probability, it delivers a description of consecutive polymerization process. This algorithm is validated by modeling the process of individual styrene monomers polymerizing into polystyrene chains, which is proved to correctly reproduce the properties of polymers in experiments. By bridging the relationship between the generic chain growth process in CG simulations and the chemical details, the impediment to reaction can be reflected. Regarding to the kinetics, it models a polymerization process with an Arrhenius-type reaction rate coefficient. Moreover, this algorithm can model both the gradual and jump processes of the bond formation, thus it readily encompasses several kinds of previous CG models of chain growth. With conducting smooth simulations, this algorithm can be potentially applied to describe the variable macroscopic features of polymers with the process of polymerization. The algorithm details and techniques are introduced in this article. © 2016 Wiley Periodicals, Inc.

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Section: Introductionmentioning

confidence: 99%

A kinetic chain growth algorithm in coarse-grained simulations

Zhu

et al. 2016

J. Comput. Chem.

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“…The parameter

c_{A}

represents the conversion of A functional group and is calculated as the ratio of the number of reacted A groups to the initial number. Differently from the previous work,

c_{A}

will dynamically change with polymerization progress.…”

Section: Methods and Algorithmmentioning

confidence: 81%

“…They found that the diffusion of reactants greatly influences the constructed network structure. Zhang and He, by combining the reactive Monte Carlo method with the coarse‐grained molecular dynamics, showed a very good agreement of AB 2 system between the experimental results and the simulation. But the drawback of their work is that the efficiency is quite low due to the lack of parallelization in the implementation of their algorithm.…”

Section: Introductionmentioning

confidence: 81%

“…However, the interaction parameters between monomer and polymer are still missing. We use a combination rule to get those parameters by using a combination rule, defined in Equation

P_{MP} = false(1 - c_{A} false) P_{M} + c_{A} P_{p}

where

P_{M}

P_{P}

, and

P_{MP}

are the potentials for the monomer, polymer, and monomer–polymer, respectively. The parameter

c_{A}

represents the conversion of A functional group and is calculated as the ratio of the number of reacted A groups to the initial number.…”

Section: Methods and Algorithmmentioning

confidence: 99%

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A Parallel Multiscale Simulation Framework for Complex Polymerization: AB₂‐Type Monomer Hyperbranched Polymerization as an Example

Zhang

Krajniak

Samaey

et al. 2018

Advcd Theory and Sims

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A multiscale simulation framework for polycondensation and other types of polymerization mechanisms, which is used to study the hyperbranched polymerization of AB 2 type monomer 4,4-bis-(4'-hydroxyphenyl)pentanoic acid is presented. The reaction framework is established by combining the coarse-grained molecular dynamics and the reactive Monte Carlo method, in which the coarse-grained molecular dynamics handles the diffusion of particles and the reactive Monte Carlo method determines the bond formation. The influence of parameters in the simulation model on the quality of the simulation results is explicitly evaluated. Specific guidelines on how to choose proper values for the simulation model parameters are given here. It is shown that i) the reaction interval should be chosen from the ballistic region of the diffusion profile; and ii) the first valley of the radial distribution function of reactants is a good value for the reaction capture radius for polycondensation in terms of accuracy of the calculations. Moreover, a recipe to determine the rate constant from the kinetic equation is explicitly proposed. Using the guidelines, excellent agreement between simulation results and the experimental data is obtained.

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“…Apart from using fine‐scale atomistic force‐fields, coarse‐grained force fields (CGFF) are recently used to build polymer systems . There are multiple major advantages of modeling polymer networks at a coarse‐grained level, compared to the atomistic level.…”

Section: Introductionmentioning

confidence: 99%

Reverse mapping method for complex polymer systems

et al. 2017

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We present a comprehensive approach for reverse mapping of complex polymer systems in which the connectivity is created by the simulation of chemical reactions at the coarse-grained scale. Within the work, we use a recently developed generic adaptive reverse mapping procedure that we adapt to handle the varying connectivity structure resulting from the chemical reactions. The method is independent of the coarse-grained and fine-grained force-fields by design and relies only on a single control parameter. We employ the approach to reverse map four different systems: a three-component epoxy network, a trimethylol melamine network, a hyperbranched polymer, and a polyethylene terephthalate. In the case of the epoxy network, we use two fine-scale representations: fully atomistic and united-atom. Whereas the trimethylol melamine network the hyperbranched polymer and the polyethylene terephthalate are reverse mapped to the fully atomistic description. After the reverse mapping, we examine the fine-grained structure by comparing the radial distribution functions with respect to the control parameter. Moreover, in the case of the epoxy we perform tensile-test experiments and examine the resulting Young's modulus. In all cases, we show how the properties of the reverse mapped systems depend on the control parameters. In general, we see that the results are relatively insensitive to the control parameter and the resulting atomistic systems are stable. Only for the trimethylol melamine network, we notice chemically incorrect conformations when the reverse mapping is performed too fast. We provide a remedy for this issue. © 2017 Wiley Periodicals, Inc.

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A coarse-grained molecular dynamics – reactive Monte Carlo approach to simulate hyperbranched polycondensation

Cited by 13 publications

References 97 publications

A kinetic chain growth algorithm in coarse-grained simulations

A kinetic chain growth algorithm in coarse-grained simulations

A Parallel Multiscale Simulation Framework for Complex Polymerization: AB₂‐Type Monomer Hyperbranched Polymerization as an Example

Reverse mapping method for complex polymer systems

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A coarse-grained molecular dynamics – reactive Monte Carlo approach to simulate hyperbranched polycondensation

Cited by 13 publications

References 97 publications

A kinetic chain growth algorithm in coarse-grained simulations

A kinetic chain growth algorithm in coarse-grained simulations

A Parallel Multiscale Simulation Framework for Complex Polymerization: AB2‐Type Monomer Hyperbranched Polymerization as an Example

Reverse mapping method for complex polymer systems

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Product

Resources

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A Parallel Multiscale Simulation Framework for Complex Polymerization: AB₂‐Type Monomer Hyperbranched Polymerization as an Example