(747h) Automation of an Energy Renormalization Approach for the Temperature Transferable Coarse-Graining of Glass-Forming Polymers
AIChE Annual Meeting
Thursday, November 2, 2017 - 4:39pm to 4:51pm
In this talk, we describe the implementation of the above algorithm in a computational âworkbenchâ we are developing to provide an integrated computational and data environment to support the development of coarse-grained force-fields using quantitative, progressive methods. The workbench environment has three essential elements: a modular program structure that supports the addition of new functionality through Python scripting ; a hierarchical data structure which enables unified representation of materials at different levels of granularity; an ontology based database environment. The starting point for the procedure is a set of all-atom (AA) simulation data at state points that span from the melt state to the glassy regime that are stored in the database as reference data. Starting with this upstream data, we then develop a workflow using the workbench environment to automated the parametrization of the ER potential using multiple properties derived from the AA simulation set. The theory and procedure are illustrated using a number of examples.
1. Wenjie Xia, Jake Song, Cheol Jeong, David D. Hsu, , Frederick R. Phelan Jr., Jack F. Douglas, and Sinan Keten, "Energy-Renormalization for Achieving Temperature Transferable Coarse-Graining of Polymer Dynamics," submitted to Proceedings of the National Academy of Sciences (PNAS) 2017.
2. Jake Song, David D. Hsu, Kenneth R. Shull, Frederick R. Phelan Jr., Jack F. Douglas, Wenjie Xia, Sinan Keten, "Energy Renormalization Method for the Coarse-Graining of Polymer Viscoelasticity," in preparation, (2017).