Systematic Coarse-graining of Epoxy Resins with Machine Learning-Informed Energy Renormalization.

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/flexible calibration functions. The framework established provides an efficient methodology for chemistry-specific, large-scale investigations of the dynamics and mechanics of epoxy resins.

Energy renormalization for coarse-graining polymers having different segmental structures.

Multiscale coarse-grained (CG) modeling of soft materials, such as polymers, is currently an art form because CG models normally have significantly altered dynamics and thermodynamic properties compared to their atomistic counterparts. We address this problem by exploiting concepts derived from the generalized entropy theory (GET), emphasizing the central role of configurational entropy s c in the dynamics of complex fluids. Our energy renormalization (ER) method involves varying the cohesive interaction strength in the CG models in such a way that dynamic properties related to s c are preserved. We test this ER method by applying it to coarse-graining polymer melts (i.e., polybutadiene, polystyrene, and polycarbonate), representing polymer materials having a relatively low, intermediate, and high degree of glass "fragility". We find that the ER method allows the dynamics of the atomistic polymer models to be faithfully described to a good approximation by CG models over a wide temperature range.

Effect of Polymer Chemistry on Chain Conformations in Hairy Nanoparticle Assemblies.

Matrix-free, polymer-grafted nanoparticles, called hairy nanoparticle assemblies (aHNPs), have proven advantageous over traditional nanocomposites, as good dispersion and structural order can be achieved. Recent studies have shown that conformational changes in the polymer structure can lead to significant enhancements in the mechanical properties of aHNPs. To quantify how polymer chemistry affects the chain conformations in aHNPs, here we present a comparative analysis based on coarse-grained molecular dynamics simulations. Specifically, we compare the chain conformations in an anisotropic cellulose nanoparticle grafted to four common polymers with distinct chemical groups, fragility, and segmental structures, that is, poly(methyl methacrylate) (PMMA), polystyrene (PS), polycarbonate (PC), and polybutadiene (PB). We observe that semiflexible glassy polymers such as PMMA and PS have a higher critical chain length (Ncr), the transition point where the polymer conformation changes from concentrated to semidilute brush regime. Flexible rubbery polymers (PB) can overcome the Ncr barrier at relatively lower molecular weights. We have used theoretical scaling laws based on Daoud-Cotton theory to uncover a direct correlation between empirical constants and physical parameters, such as persistence length and monomer excluded volume. Furthermore, we carried out a systematic study to understand the role of backbone rigidity and side-group size of polymer, and it revealed that the backbone rigidity significantly affects Ncr but the side-group size doesn't seem to have an appreciable effect on Ncr. We find that normalization of the monomer radial distribution curves using Ncr and other key molecular parameters collapses the curves for 110 distinct model aHNP systems studied. Our work paves the way for systematic quantification of these molecular design parameters to accelerate the design of polymer-grafted nanoparticle assemblies in combination with universal scaling relationships.

Materials by Design for Stiff and Tough Hairy Nanoparticle Assemblies.

Matrix-free polymer-grafted nanocrystals, called assembled hairy nanoparticles (aHNPs), can significantly enhance the thermomechanical performance of nanocomposites by overcoming nanoparticle dispersion challenges and achieving stronger interfacial interactions through grafted polymer chains. However, effective strategies to improve both the mechanical stiffness and toughness of aHNPs are lacking given the general conflicting nature of these two properties and the large number of molecular parameters involved in the design of aHNPs. Here, we propose a computational framework that combines multiresponse Gaussian process metamodeling and coarse-grained molecular dynamics simulations to establish design strategies for achieving optimal mechanical properties of aHNPs within a parametric space. Taking poly(methyl methacrylate) grafted to high-aspect-ratio cellulose nanocrystals as a model nanocomposite, our multiobjective design optimization framework reveals that the polymer chain length and grafting density are the main influencing factors governing the mechanical properties of aHNPs, in comparison to the nanoparticle size and the polymer-nanoparticle interfacial interactions. In particular, the Pareto frontier, that marks the upper bound of mechanical properties within the design parameter space, can be achieved when the weight percentage of nanoparticles is above around 60% and the grafted chains exceed the critical length scale governing transition into the semidilute brush regime. We show that theoretical scaling relationships derived from the Daoud-Cotton model capture the dependence of the critical length scale on graft density and nanoparticle size. Our established modeling framework provides valuable insights into the mechanical behavior of these hairy nanoparticle assemblies at the molecular level and allows us to establish guidelines for nanocomposite design.

Energy Renormalization for Coarse-Graining the Dynamics of a Model Glass-Forming Liquid.

Coarse-grained modeling achieves the enhanced computational efficiency required to model glass-forming materials by integrating out "unessential" molecular degrees of freedom, but no effective temperature transferable coarse-graining method currently exists to capture dynamics. We address this fundamental problem through an energy-renormalization scheme, in conjunction with the localization model of relaxation relating the Debye-Waller factor ⟨u2⟩ to the structural relaxation time τ. Taking ortho-terphenyl as a model small-molecule glass-forming liquid, we show that preserving ⟨u2⟩ (at picosecond time scale) under coarse-graining by renormalizing the cohesive interaction strength allows for quantitative prediction of both short- and long-time dynamics covering the entire temperature range of glass formation. Our findings provide physical insights into the dynamics of cooled liquids and make progress for building temperature-transferable coarse-grained models that predict key properties of glass-forming materials.