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Using molecular simulations and theory, we develop an explicit mapping of thecontribution of molecular relaxation modes in glassy thermosets to the shearmodulus, where the relaxations were tuned by altering the polarity of sidegroups. Specifically, motions at the domain, segmental, monomer, and atomiclevels are taken from molecular dynamics snapshots and directly linked with theviscoelasticity through a framework based in the lattice dynamics of amorphoussolids. This unique approach provides direct insight into the roles of chemicalgroups in the stress response, including the timescale and spatial extent ofrelaxations during mechanics. Two thermoset networks with differingconcentrations of polar side groups were examined, dicyclopentadiene (DCPD) and5-norbornene-2-methanol (NBOH). A machine learning method is found to beeffective for quantifying large-scale correlated motions while more localchemical relaxations are readily identified by direct inspection. The approachis broadly applicable and enables rapid predictions of the frequency-dependentmodulus for any glass.

Identifying non-affine softening modes in glassy polymer networks: A pathway to chemical design