Multiscale Modeling Scheme for Simulating Polymeric Melts: Application to Poly(Ethylene Oxide)

Abstract

polyolefins, from which the volumetric expansive coefficients and glass transition temperatures (T g ) could be determined. Up to now, this method has been extensively applied to complex polymers, [4] including dendrimers [5,6] and crosslinked networks, [7,8] to examine effects of various factors (i.e., molecular weight, [9] tacticity, [10] confinement [11] ) on the volumetric properties.However, in order to achieve repeatable results, adequate thermodynamics equilibrium should be usually attained in these AA MD simulations. Although a great variety of schemes have been proposed to improve the sampling efficiency, restrained by the current computational power, the productive AA MD simulations are only generally suitable for investigating short-term behaviors of small short-chain polymer (namely, oligomer) systems. To study specific polymer systems at the wide timeand space-scales, one of quite promising schemes is multiscale modeling, which groups every AA segment into one super-atom or coarse-grained (CG) bead. [12,13] In this scheme, it is critical to obtain the "potential energy" (referred to free energy or potential of mean force in most cases) that can accurately describe chain structure and interbead interactions. [14,15] Numerous CG methods have been developed, [16] among which most typical ones include iterative Boltzmann inversion (IBI), [17] force match, [18][19][20] inverse Monte Carlo, [21] conditional reversible work (CRW), [22,23] and MARTINI, [24] etc. Mainly, these methods differ in the target functions that are used to parameterize the nonbonded CG potentials. For example, the IBI method [17] adopts the structural distributions, the CRW method [22] uses the interaction free energy, and the MARTINI method [24] aims to reproduce the partitioning free energies.In general, the CG potentials parameterized until now suffer from representability and transferability. [16,25,26] Namely, even at the same state as the parameterized one with some properties, simulations with these CG potentials cannot reproduce other properties; and CG potentials that are parameterized at some state cannot reliably be used at other states. In order to improve the predictive power of CG simulations, numerous studies have been carried out during the past decade. Here only some of typical ones are mentioned. As one of early works, Qian et al. [27] modified the IBI potentials by a temperature factor to reproduce the thermal expansion coefficients over a broad range of temperature. This method was called one-point extrapolation Multiscale SimulationsThe poly(ethylene oxide) (PEO) is employed as one typical example to demonstrate a new multiscale modeling scheme for simulating high-molecularweight polymeric melts. In this scheme, the structural distributions and the densities at five elevated temperatures at 1 atm, which are obtained from molecular dynamics (MD) simulations of all-atomistic oligomeric melt, are employed as the target functions to parameterize the coarse-grained (CG) potentials. The extensive CG MD simulations rep...

Rationalizing the Phase Behavior of Triblock Copolymers through Experiments and Molecular Simulations

“…A number of computational studies addressed the behavior of (PPG)m(PEG)n units at different levels of theory, from ab initio quantum calculations [11][12][13][14] to classical all-atom (AA) molecular dynamics (MD) or Monte Carlo (MC) simulations. [15][16][17][18][19][20][21][22][23][24][25][26][27] Coarse-Grained (CG), 3,19,23,[27][28][29][30][31][32][33][34][35][36][37] Mean-Field Coarse-Grained (MF-CG) 38 or dissipative particle dynamics (DPD) 39-42 simulations were also deployed to investigate mesoscale structures of these polymers.…”

“…Nevertheless, despite the ability of the CG approach to qualitatively map the entire phase behavior of triblock copolymers 36 or to form complex structures in diblock copolymers such as bilayers or vesicles, 39 they have been hindered by the lack of transferability of the models. 19 The development of the MARTINI model 34,45 provides a generally applicable CG framework in which the molecules can be mapped based on an energy matrix of interactions with 18 different bead types. 45 The CG models based on the MARTINI force field were able to address the complexity of the self-assembly process of many amphiphilic molecules [46][47][48][49][50][51] as well as copolymer solutions.…”

Investigation of the Rheological Behaviors of Polymeric Materials in the Film Casting Process through Multiscale Modeling and Simulation Method

“…The viscoelastic properties of the bulk‐like polymer were firstly obtained from molecular dynamics simulations and the stress–relaxation simulations of the composites were further performed with material‐point method to extract the viscoelastic properties. Wu proposed a multiscale modeling scheme for polymeric melts which was successfully applied to poly(ethylene oxide). The coarse‐grained potentials parameterized using the developed multiscale scheme could accurately simulate the volumetric properties including the density and the expansion coefficients while capturing some essential conformational properties for the high‐molecular‐weight polymer melts.…”

Natural Rubber with Polyhedral Oligomeric Silsesquioxane, Nanocomposites, and Hybrids Compared by Molecular Modeling

“…While experimental results for mixtures are preferred, modeling is used to predict structure and properties of similar materials because it eliminates the need for synthesis of each material composition, confirming proposed hypotheses, while obtaining principally new results . Computational performance increases each year and in novel modeled synthetic methods, materials can gain complexity, and molecular dynamics has emerged as an attractive tool . The use of molecular modeling and simulation becomes more accessible, and this is especially true in the nanocomposite field, where design rules allow modeling of engineering materials by combining the desirable properties of nanoparticles and polymers for potential commercialization …”

“…[21] Computational performance increases each year and in novel modeled synthetic methods, materials can gain complexity, and molecular dynamics has emerged as an attractive tool. [22] The use of molecular modeling and simulation becomes more accessible, and this is especially true in the nanocomposite field, where design rules allow modeling of engineering materials by combining the desirable properties of nanoparticles and polymers for potential commercialization. [23] There are many software programs developed to predict properties of polymers that use different methods; the program Synthia [24] is similar to traditional group additive contribution methods, but the calculated properties are expressed in terms of topological variables, (connectivity indices) combined with geometrical variables and structural descriptors, allowing the prediction of properties of truly novel types of polymer structures.…”