Molecular and Mesoscale Simulation Methods for Polymer Materials

Abstract

Polymers offer a wide spectrum of possibilities for materials applications, in part because of the chemical complexity and variability of the constituent molecules, and in part because they can be blended together with other organic as well as inorganic components. The majority of applications of polymeric materials is based on their excellent mechanical properties, which arise from the long-chain nature of the constituents. Microscopically, this means that polymeric materials are able to respond to external forces in a broad frequency range, i.e., with a broad range of relaxation processes. Computer simulation methods are ideally suited to help to understand these processes and the structural properties that lead to them and to further our ability to predict materials properties and behavior. However, the broad range of timescales and underlying structure prohibits any one single simulation method from capturing all of these processes. This manuscript provides an overview of some of the more popular computational models and methods used today in the field of molecular and mesoscale simulation of polymeric materials, ranging from molecular models and methods that treat electronic degrees of freedom to mesoscopic field theoretic methods.

Influence of nanoparticle size, loading, and shape on the mechanical properties of polymer nanocomposites

“…[16][17][18][19][20][21][22][23][24][25][26] Concerning the nanofiller size and shape, computational studies have focused on fillers whose size is comparable to the characteristic length scales of the polymer matrix, namely the radius of gyration of the polymers or the size of their monomers. 21 mal NP dispersion and strong polymer-NP interactions 27 have shown that the smaller NPs have better reinforcing properties, leading to tougher PNCs.…”

A new multiscale modeling method for simulating the loss processes in polymer solar cell nanodevices

“…[49][50][51] However, we can safely predict that numerous polymer solar cell devices will only hardly be treatable within the conventional particle description, due to the large system sizes and long chain lengths encountered in most polymer applications. 47 To study the influence of defects on the solar cell performance, we developed a new simulation algorithm, which makes use of both either the time-dependent GinzburgLandau (TDGL) method or the self-consistent field theory (SCFT) method, 52 to generate the nanoscale morphology of the polymer blend, in conjunction with the dynamic Monte Carlo method, to mimic the elementary photovoltaic processes. To obtain morphologies of different interfacial lengths, we describe the non-equilibrium dynamics of the phase-separation process using the TDGL method.…”

“…To obtain morphologies of different interfacial lengths, we describe the non-equilibrium dynamics of the phase-separation process using the TDGL method. The method is based on the Cahn-Hilliard-Cook (CHC) nonlinear diffusion equation for a binary mixture, which for an incompressible binary AB-polymer blend results in the following equation of motion: 52,53 ∂φ(r, t) ∂t…”

Liquid-crystal phase equilibria of Lennard-Jones chains

“…Simulations based on coarse-grained models can access longer time and length scales than their atomistic counterparts, allowing a bulk description of fluids. Coarse-grained models are commonly used to represent a simplified picture of large molecules, such as biomolecules [12][13][14][15][16], polymers [17][18][19][20][21][22], or liquid crystals [23][24][25][26][27][28]. Moreover, simulation results obtained from coarse-grained models can be directly compared with theoretical predictions that are based on a well-defined Hamiltonian, such as the family of perturbation theories developed from the statistical association fluid theory (SAFT) [29][30][31][32][33][34][35][36].…”