Materials by design for hairy nanoparticle assemblies

A computational framework that combines machine learning and coarse-grained molecular dynamics (CGMD) simulations to tailor the mechanical properties of hairy nanoparticle assemblies (aHNP) is proposed. CGMD informed metamodel on PMMA-grafted nanocellulose assemblies revealed the necessity of having relatively low grafting density, high molecular weight, and high nanoparticle loading on achieving Pareto optimality. Utilizing theoretical scaling relationships derived from Daoud−Cotton theory, we identified the critical length scale (N_cr) governing Pareto optimality, originating based on conformational transition from concentrated to semi-dilute brush regime. We verified this finding by expanding our analysis to other common polymers, and quantified the role of polymer chemistry, backbone rigidity and side-group size of a polymer on the chain conformations and N_cr. Remarkably, normalization of the monomer radial distribution curves using N_cr and other key molecular parameters collapsed the curves for 110 distinct model aHNP systems to a universal curve governing the chain conformations in aHNPs. The novel modeling framework along with the new physical insights into the confinement and interface effects in aHNPs pave the way for superior designs of aHNPs.