Advanced manufacturing demands faster and more accurate molecular models for the development of new polymeric materials for applications such as lightweight vehicle components. Atomistic-level information is required to predict chemistry-specific temperature dependence such as the transition from a melt to a glass, but fully atomistic simulation systems are computationally expensive. Numerous coarse-graining (CG) simulation approaches have been developed to study polymers by lumping several atoms into fewer CG sites and enable the study of polymer dynamics over reasonable timescales. However, most current CG models are limited to a specific state point (i.e., they are not temperature-transferable), rendering them unable to predict properties during manufacturing or operating conditions. Here, we build on a recently developed temperature-transferable, coarse-graining algorithm that captures chemistry-specific dynamics from the melt to the glass, the Energy Renormalization (ER) scheme [1,2]. We study the extension of the ER scheme to higher levels of CG by additionally incorporating a dissipative term typically found in higher-level CG models into the molecular potential. We demonstrate that such a higher-level CG scheme still retains temperature transferability (i.e., recovery of temperature-dependent polymer dynamics) and enables improved recovery of structure.
 Wenjie Xia, Jake Song, Cheol Jeong, David D. Hsu, Frederick R. Phelan Jr., Jack F. Douglas, Sinan Keten, âEnergy-Renormalization for Achieving Temperature Transferable Coarse-Graining of Polymer Dynamics,â Macromolecules, 50 (21), pp 8787â8796, (2017).
 Wenjie Xia, Jake Song, Nitin H. Krishnamurthy, Frederick R. Phelan Jr., Sinan Keten, Jack F. Douglas, âEnergy Renormalization to Coarse-Graining of the Dynamics of a Model Glass-Forming Liquid,â Journal of Physical Chemistry B, 122 (6), pp 2040â2045 (2018).