  This work presents a novel framework governing the development of an
efficient, accurate, and transferable coarse-grained (CG) model of a polyether
material. The proposed framework combines the two fundamentally different
classical optimization approaches for the development of coarse-grained model
parameters; namely bottom-up and top-down approaches. This is achieved through
integrating the optimization algorithms into a machine learning (ML) model,
trained using molecular dynamics (MD) simulation data. In the bottom-up
approach, bonded interactions of the CG model are optimized using deep neural
networks (DNN), where atomistic bonded distributions are matched. The atomistic
distributions emulate the local chain structure. In the top-down approach,
optimization of nonbonded potentials is accomplished by reproducing the
temperature-dependent experimental density. We demonstrate that CG model
parameters achieved through our machine-learning enabled hybrid optimization
framework fulfills the thermodynamic consistency and transferability issues
associated with the classical approaches to coarse-graining model polymers. We
demonstrate the efficiency, accuracy, and transferability of the developed CG
model, using our novel framework through accurate predictions of chain size as
well as chain dynamics, including the limiting behavior of the glass transition
temperature, diffusion, and stress relaxation spectrum, where none were
included in the potential parameterization process. The accuracy of the
predicted properties are evaluated in the context of molecular theories and
available experimental data.
