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Abstract
In this paper, we identify the modifications needed in a recently developed generic coarse-grained (CG) model that captured directional interactions in polymers to specifically represent two exemplary hydrogen bonding polymer chemistries—poly(4-vinylphenol) and poly(2-vinylpyridine). We use atomistically observed monomer-level structures (e.g., bond, angle and torsion distribution) and chain structures (e.g., end-to-end distance distribution and persistence length) of poly(4-vinylphenol) and poly(2-vinylpyridine) in an explicitly represented good solvent (tetrahydrofuran) to identify the appropriate modifications in the generic CG model in implicit solvent. For both chemistries, the modified CG model is developed based on atomistic simulations of a single 24-mer chain. This modified CG model is then used to simulate longer (36-mer) and shorter (18-mer and 12-mer) chain lengths and compared against the corresponding atomistic simulation results. We find that with one to two simple modifications (e.g., incorporating intra-chain attraction, torsional constraint) to the generic CG model, we are able to reproduce atomistically observed bond, angle and torsion distributions, persistence length, and end-to-end distance distribution for chain lengths ranging from 12 to 36 monomers. We also show that this modified CG model, meant to reproduce atomistic structure, does not reproduce atomistically observed chain relaxation and hydrogen bond dynamics, as expected. Simulations with the modified CG model have significantly faster chain relaxation than atomistic simulations and slower decorrelation of formed hydrogen bonds than in atomistic simulations, with no apparent dependence on chain length.
Highlights
Advances in modeling and simulation of polymers over the past few decades have enabled many valuable studies of macromolecular materials over a broad range of relevant length and time scales—from oscillations in bonds and angles at the monomer level, to relaxation and diffusion at the chain level, to the assembly of chains into ordered domains [1,2,3,4,5,6,7,8,9,10,11,12]
CG polymer models can be developed in a bottom-up manner by using microscopic data from atomistic simulations to obtain all bonded and non-bonded CG model parameters via techniques like the iterative Boltzmann inversion (IBI) [56,57,58,59,60,61,62,63], inverse Monte Carlo (IMC) [64,65,66,67], multiscale coarse-graining (MS-CG) [51,68,69,70], relative entropy [71,72,73,74,75,76], generalized Yvon–Born–Green [77,78] method, and conditional reversible work [79,80,81,82] method
As our CG model is extended from the generic CG model of Kulshreshtha et al [109], we first compare the structures generated by the CG model of Kulshreshtha et al [109], denoted as the “original”
Summary
Advances in modeling and simulation of polymers over the past few decades have enabled many valuable studies of macromolecular materials over a broad range of relevant length and time scales—from oscillations in bonds and angles at the monomer level, to relaxation and diffusion at the chain level, to the assembly of chains into ordered domains [1,2,3,4,5,6,7,8,9,10,11,12]. This generic CG model of Kulshreshtha et al, capturing directional interactions in polymers, enabled simulation studies of universal structural behavior common to many hydrogen bonding polymers independent of specific polymer chemistry This generic CG model of Kulshreshtha et al [109] did not include specific bonded constraints (e.g., angle or dihedral potentials to mimic local orientational penalty) that could alter the ability to form a hydrogen bond between two monomers. Rather than conduct a bottom-up development for a completely new CG model using atomistic to CG mapping approaches described earlier, we want to demonstrate in an incremental step-by-step manner what few modifications (e.g., intra-chain interactions and torsional constraint) are needed in the generic CG model of Kulshreshtha et al [109] to reproduce the atomistic structure of these polymer chemistries We achieve this for the chain length of 24 monomers.
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