Abstract
A quantitatively accurate prediction of properties for entangled polymers is a long-standing challenge that must be addressed to enable efficient development of these materials. The complex nature of polymers is the fundamental origin of this challenge. Specifically, the chemistry, structure, and dynamics at the atomistic scale affect properties at the meso and macro scales. Therefore, quantitative predictions must start from atomistic molecular dynamics (AMD) simulations. Combined use of atomistic and coarse-grained (CG) models is a promising approach to estimate long-timescale behavior of entangled polymers. However, a systematic coarse-graining is still to be done for bridging the gap of length and time scales while retaining atomistic characteristics. Here we examine the gaps among models, using a generic mapping scheme based on power laws that are closely related to universality in polymer structure and dynamics. The scheme reveals the characteristic length and time for the onset of universality between the vastly different scales of an atomistic model of polyethylene and the bead-spring Kremer–Grest (KG) model. The mapping between CG model of polystyrene and the KG model demonstrates the fast onset of universality, and polymer dynamics up to the subsecond time scale are observed. Thus, quantitatively traceable timescales of polymer MD simulations can be significantly increased.
Highlights
Entangled polymers are widely used in many industrial applications
The simulations were a combination of atomistic molecular dynamics (AMD) and middle-level CGMD that are linked with a specialized mapping scheme
Our mapping scheme was applied to estimate the gap between multiscale MD (MSMD) data and that of KGMD
Summary
Entangled polymers are widely used in many industrial applications. Despite many years of basic polymer research and industrial use, quantitatively accurate predictions of polymer properties is a long-standing problem that is of great importance for efficient improvement of those properties. While recent advances in computation have enabled a wide range of AMD simulations of chemical and biological polymers[4,5,6,7,8,9,10,11,12], fully atomistic simulations of entangled polymer dynamics over long timescales are beyond the capability of current platforms. With the exception of plain molecular geometries and excluded volume effects, chemical details are entirely omitted in the KG model Despite its simplicity, it is highly useful and has been used in a wide range of studies on polymer nanocomposites[32,33], polymer welding[34], polymer brushes[35,36,37], poly-electrolyte gels[38], thermoresponsive polymers[39], ring polymers[40], polymer collapse[41], healing of polymer interfaces[42,43], and biopolymeric motions[44]. The gaps provide useful information for determination of appropriate CG level, showing a significant advance that will enable a quantitative solution for the challenging problems described above
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