(747h) Automation of an Energy Renormalization Approach for the Temperature Transferable Coarse-Graining of Glass-Forming Polymers

A key objective of the Materials Genome Initiative (MGI) for soft materials is to create a coherent framework to perform coarse-grained (CG) simulations in a streamlined and efficient manner to reduce the time and cost of novel material discovery and design. Such an effort involves both advancement in coarse-graining theory, and development of appropriate machinery to give effective practical implementation. Recently, we proposed a coarse-graining strategy for glass forming (GF) liquids based on an energy renormalization (ER) scheme in which cohesive interactions are rescaled using the altered configurational entropy [1,2]. This approach yields a temperature dependent coarse-grained potential with simple form that preserves atomistic dynamics and GF activation energy from the high-temperature melt state to the low-temperature glassy state.

In this talk, we describe the implementation of the above algorithm in a computational “workbench” 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.

References

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).