  A persistent challenge in predictive molecular modeling of thermoset polymers
is to capture the effects of chemical composition and degree of crosslinking
(DC) on dynamical and mechanical properties with high computational efficiency.
We established a new coarse-graining (CG) approach that combines the energy
renormalization method with Gaussian process surrogate models of the molecular
dynamics simulations. This allows a machine-learning informed functional
calibration of DC-dependent CG force field parameters. Taking versatile epoxy
resins consisting of Bisphenol A diglycidyl ether combined with curing agent of
either 4,4-Diaminodicyclohexylmethane or polyoxypropylene diamines, we
demonstrated excellent agreement between all-atom and CG predictions for
density, Debye-Waller factor, Young's modulus and yield stress at any DC. We
further introduce a surrogate model enabled simplification of the functional
forms of 14 non-bonded calibration parameters by quantifying the uncertainty of
a candidate set of high-dimensional and flexible calibration functions. The
framework established provides an efficient methodology for chemistry-specific,
large-scale investigations of the dynamics and mechanics of epoxy resins.
