Multiscale Molecular Modeling of polymer-based additive manufacturing*

Light-driven and photocurable polymer-based additive manufacturing (AM) has enormous potential due to its excellent resolution and precision avoiding the typical layer by layer approach. Acrylated resins that undergo radical chain-growth polymerization are widely used in photopolymer AM due to their fast kinetics and serve as a departure point for developing other resins for photopolymer-based AM technologies. For successful control of the resins, the molecular basis of the acrylate free-radical polymerization has to be understood in detail. We are using an integrated multiscale approach to reproduce computationally the reactive process. We present an optimized reactive force field (ReaxFF) for MD simulations of acrylate polymer resins that captures radical polymerization thermodynamics and kinetics. It was critical to train the force field against an incorrect, nonphysical reaction pathway. The resulting model describes polymer resin formation, crosslinking density, conversion rate, and residual monomers of the complex acrylate mixtures. To scale up we present a general approach to isolate chemical reaction mechanism as a controllable variable across chemically distinct systems. Modern approaches to reduce computational expense of MD simulations often group multiple atoms into a single “coarse-grained” interaction site, leading to a loss of resolution. Here we convert this shortcoming into a feature and use identical coarse-grained models to represent molecules that share nonreactive characteristics but react by different mechanisms. We use this approach to simulate and investigate distinct, yet similar, trifunctional isocyanurate resin formulations that polymerize by either chain- or step-growth. Because the underlying molecular mechanics of these models are identical, all emergent differences are a function of the reaction mechanism only. We find that the microscopic morphologies resemble related all-atom simulations and that simulated mechanical testing reasonably agrees with experiment.