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Energy Renormalization Method for the Coarse-Graining of Polymer Viscoelasticity

Published

Author(s)

Jake Song, David D. Hsu, Kenneth R. Shull, Frederick R. Phelan Jr., Jack F. Douglas, Wenjie Xia, Sinan Keten

Abstract

Developing time and temperature transferable coarse-grained (CG) models is essential for the computational prediction and design of polymeric glass-forming materials, but this goal has remained elusive. The dynamics of CG models are often greatly altered from those of all- atomistic (AA) models, and this change and corresponding difference in properties is directly manifested by the diverging activation energies of glass-formation between the two models. Recently, we have introduced an energy renormalization (ER) strategy that corrects the divergent enthalpy of activation of CG models, and preserve the temperature-dependent dynamics of the AA model. Here we apply our ER method for modeling polymer viscoelasticity, which requires a consideration of the frequency-dependence of structural relaxation that occurs during glass-formation. We showcase this approach on two rather different polymers, polybutadiene and polystyrene, which are strong and fragile glass-formers, respectively. By accounting for the frequency-dependence of glass-formation dynamics, we show that our ER method allows us to match the viscoelastic shear response of AA models of these disparate polymer systems. The ER method systematically fixes the activation energy of reduced-order models over a wide temperature range, and provides quantitative representation of polymer viscoelasticity at greater time-scales.
Citation
Macromolecules
Volume
51
Issue
10

Keywords

coarse-grained modeling, molecular dynamics, transferability, polymers, polymer glasses, viscoelasticity

Citation

Song, J. , Hsu, D. , Shull, K. , Phelan Jr., F. , Douglas, J. , Xia, W. and Keten, S. (2018), Energy Renormalization Method for the Coarse-Graining of Polymer Viscoelasticity, Macromolecules, [online], https://doi.org/10.1021/acs.macromol.7b02560, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=923280 (Accessed September 27, 2022)
Created May 9, 2018, Updated October 12, 2021