(109e) Molecular Modeling for Additive Manufacturing

Additive Manufacturing in its many forms – 3D printing is probably the most well-known variant – is revolutionizing the industrial manufacturing process. Still, in order for it to realize its full potential additional fundamental studies are needed. Here I am discussing several recent computational efforts to improve our understanding of modern additive manufacturing with a focus on soft materials which are hugely important for the future development in many areas from artificial tissue to energy harvesting.

Phospholipids are one of the most important biomolecules as they make up the membranes in all cells. Here we are presenting an integrated molecular simulation and experimental effort to understand the printing of lipids on different substrates with various hydrophilicities. We are using the Martini simulation model to study the assembly of lipids on various substrates during the drying process in order to understand the stacking and arrangement as a function of surface chemistry as well as concentration. In parallel experimental efforts, lipids are deposited using microfluidic probes and characterized using AFM to control and reveal lipid assembly, respectively.

Light-driven and photocurable polymer-based additive manufacturing (AM) has enormous potential due to its excellent resolution and precision and 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 often serve as a departure point for developing other resin materials for photopolymer-based AM technologies. For successful control of the photopolymer 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 molecular dynamics (MD) simulations of acrylate polymer resins that captures radical polymerization thermodynamics and kinetics. We also found that it was critical to train the force field against an incorrect, nonphysical reaction pathway observed in simulations that used parameters not optimized for acrylate polymerization. The resulting model can describe 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 an independently controllable variable across chemically distinct systems. Modern approaches to reduce the computational expense of molecular dynamics simulations often group multiple atoms into a single “coarse-grained” interaction site, which leads to a loss of chemical 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.