Resolving Dynamic Properties of Polymers through Coarse-Grained Computational Studies

Abstract

Coupled length and time scales determine the dynamic behavior of polymers and underlie their unique viscoelastic properties. To resolve the long-time dynamics it is imperative to determine which time and length scales must be correctly modeled. Here we probe the degree of coarse graining required to simultaneously retain significant atomistic details and access large length and time scales. The degree of coarse graining in turn sets the minimum length scale instrumental in defining polymer properties and dynamics. Using linear polyethylene as a model system, we probe how coarse graining scale affects the measured dynamics. Iterative Boltzmann inversion is used to derive coarse-grained potentials with 2-6 methylene groups per coarse-grained bead from a fully atomistic melt simulation. We show that atomistic detail is critical to capturing large scale dynamics. Using these models we simulate polyethylene melts for times over 500 µs to study the viscoelastic properties of well-entangled polymer chains.Polymer properties depend on a wide range of coupled length and time scales, with unique viscoelastic properties stemming from interactions at the atomistic level. The need to probe polymers across time and length scales to capture polymer behavior makes probing dynamics, and particularly computational modeling, inherently challenging. With increasing molecular weight, polymer melts become highly entangled and the longtime diffusive regime becomes computationally inaccessible using atomistic simulations. In these systems the diffusive time scale increases with polymerization number N faster than N 3 , becoming greater than 10 10 times larger than the shortest time scales even for modest molecular weight polymers. While it is clear that the largest lengths scales of polymer dynamics are controlled by entanglements, the shortest time and length scales required to resolve dynamic properties are not obvious. This knowledge is critical for developing models that can transpose atomistic details into the long time scales needed to model long, entangled polymer chains.One path to overcoming this computational challenge is to coarse grain the polymer, reducing the number of degrees of freedom and increasing the fundamental time scale. The effectiveness of this process depends on retaining the smallest length scale essential to capturing the polymer dynamics. The process of coarse graining amounts to combining groups of atoms into pseudoatom beads and determining the bead interaction potentials [1,2]. Simple models like the bead-spring model [3], capture characteristics described by scaling theories, but disregard atomistic details and cannot quantitatively describe properties like structure, local dynamics or densities. Immense efforts have been made to systematically coarse grain polymers and bridge the gap of time and length scales while retaining atomistic characteristics [4]. One critical issue underlying the coarse graining process is the degree to which a polymer can be coarseA single C96H194 PE chain represented with in...

Energy Renormalization for Coarse‐Graining Polymers with Different Fragilities: Predictions from the Generalized Entropy Theory

“…For all the examined E b / k B , a higher degree of coarse‐graining with a smaller M generally requires greater magnitudes of ε CG to preserve the relaxation dynamics of the AA model. The significant influence of the λ on the dynamics has also been confirmed by the recent MD study by Grest et al,18 who reported that the time “scaling” factor required to correct the dynamics of polyethylene melts varies with λ (i.e., number of methylene groups per CG bead in their study). This result is in line with the recent effort by Foley et al,28 who showed that the resolution of the CG representation has a direct impact on the potential of mean force for a CG model through its influence on the loss of configurational entropy under coarse‐graining.…”

“…This can be largely attributed to the reduction of the fluid configurational entropy s c as the degrees of freedom are integrated out under coarse‐graining, which is expected to play a central role in dynamics of supercooled GF liquids 5,15,16. Although recent studies have shown several routes to apparently reproducing AA dynamics by introducing non‐conservative forces or time scaling factors,15,17–19 achieving temperature transferability has not been that successful for these CG modeling approaches, limiting their practical usages in computational design and discovery of GF materials.…”

“…Despite the success of applying this approach to coarse‐graining polymers, deriving the ER factors unfavorably requires relatively extensive AA and CG simulations at multiple temperature states, which needs to be improved. Notably, recent evidences have suggested that the key factors including degree of coarse‐graining and inherent GF properties have a huge impact on the resulting dynamics and thermodynamics of CG models 18,28. A deeper understanding of how these factors are related to the ER is of critical importance to establish a universal strategy for predictive CG modeling of polymers and other relevant materials.…”

Nonmonotonic dependence of polymer-glass mechanical response on chain bending stiffness

“…Future work might extend the present finding to coarse-grained potentials that represent more specific materials [7,27,34]. Moreover, the present result represents the limit of long chains, while future work might explore the minimum chain length required to observe nonmonotonicity.…”

Polymer and spherical nanoparticle diffusion in nanocomposites

“…However searching for the reptation power laws 1 as predicted by reptation theory, through molecular dynamics simulations, is difficult [32][33][34][35][36][37][38] . For example by using the Kremer-Grest model 31 the reptation regime can not be reached for very long polymers 33 .…”