Systematic coarse-graining of epoxy resins with machine learning-informed energy renormalization

A persistent challenge in molecular modeling of thermoset polymers is capturing the effects of chemical composition and degree of crosslinking (DC) on dynamical and mechanical properties with high computational efficiency. We established a coarse-graining (CG) approach combining the energy renormalization method with Gaussian process surrogate models of 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 introduced a surrogate model-enabled simplification of the functional forms of 14 non-bonded calibration parameters by quantifying the uncertainty of a candidate set of calibration functions. The framework established provides an efficient methodology for chemistry-specific, large-scale investigations of the dynamics and mechanics of epoxy resins.

Reverse Coarsening and the Control of Particle Size Distribution through Surfactant

The minimization of surface area, as a result of the minimization of (positive) surface energy, is a well-known driving force behind the spontaneous broadening of (nano) particle size distribution. We show that surfactant molecules binding to particle surfaces effectively decrease the surface energy and may change its sign. In this case, contrary to the expected broadening behavior, a minimum of free energy is achieved at the maximum surface area for all particles, i.e., when the particles are identical. Numerical simulations based on the classical Lifshitz–Slyozov–Wagner theory with surfactant-induced surface energy renormalization confirm the collapse of the particle size distribution. As the particle size evolution is much slower than particle nucleation and growth, the manipulation of surface energy with in-situ replacement of surfactant molecules provides a method for controlling particle size distribution with great potential for creating mono-disperse nanoparticles, a key goal of nanotechnology.