Bonded Potential Dynamics in Chemically-Specific Coarse-Grained Models of Polymers

In recent work [J. Chem. Phys. 154,084114 (2021)], we studied a coarse-grain (CG) model that aims to preserve both chemical specificity and dynamics of a polymer melt. We used iterative Boltzmann inversion (IBI) to parameterize the conservative potential and Langevin dynamics as a means to recover the all-atom (AA) dynamics. Here we further our analysis of CG models by examining the influence of bonded potential dynamics on CG diffusion. In IBI, CG potentials are parametrized such that the structural distributions match those of the AA reference system. However, after correcting CG diffusion via a Langevin friction factor, the dynamics of the CG potentials are still much faster than the AA target potential dynamics as measured by autocorrelation functions. To correct this, rescaling factors are computed and applied to the CG system. We show that rescaling to match the autocorrelation functions of the distributions brings about a better match between CG and AA dynamic properties. The effect of the refined potentials on property predictions both with both bonded potential correction and Langevin dynamics are analyzed.