Abstract
Combining atomistic and coarse-grained (CG) models is a promising approach for quantitative prediction of polymer properties. However, the gaps between the length and time scales of atomistic and CG models still need to be bridged. Here, the scale gaps of the atomistic model of polyethylene melts, the bead–spring Kremer–Grest model, and dissipative particle dynamics with the slip-spring model were investigated. A single set of spatial and temporal scaling factors was determined between the atomistic model and each CG model. The results of the CG models were rescaled using the set of scaling factors and compared with those of the atomistic model. For each polymer property, a threshold value indicating the onset of static or dynamic universality of polymers was obtained. The scaling factors also revealed the computational efficiency of each CG model with respect to the atomistic model. The performance of the CG models of polymers was systematically evaluated in terms of both the accuracy and computational efficiency.
Highlights
Because of scientific and industrial interest, entangled polymer materials have been studied for many years
The computational efficiency of each CG model was quantitatively estimated with respect to the atomistic model
For h R2 i–M, the rescaled KG MD (KGMD) results are almost equal to the Atomistic MD (AMD) results for M ≥ Mc, whereas the rescaled dissipative particle dynamics (DPD) results slightly deviate from the AMD results for M ≥ Mc
Summary
Because of scientific and industrial interest, entangled polymer materials have been studied for many years. The realistic units of length, time, temperature, and so forth are lost during such coarse graining, and coarse-grained (CG) MD (CGMD) simulations only return qualitative values These “scale gaps” make it difficult to evaluate the accuracy of the estimated polymer properties and determine how much MD simulations are accelerated using CG models. Takahashi et al [20] compared the polymer dynamics of the atomistic PE model with that of the bead-spring model They determined a set of spatial and temporal scaling factors to successfully rescale the results of the bead–spring model, but the scaling behavior around the onset of entanglement was different for each polymer property. The performance of the CG models of polymers was systematically and quantitatively evaluated in terms of both the accuracy and computational efficiency
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