Polymer-grafted nanoparticles (PGNs) have emerged as a versatile class of hybrid building blocks that can self-assemble into complex superlattices with highly tunable physical properties. While the mechanical behavior of single-component PGNs (SC-PGNs) has been extensively studied, the emergent structure and mechanics of multicomponent PGNs (MC-PGNs) remain poorly understood. In particular, whether improving packing by designer superlattices can enhance mechanical performance remains to be established. To achieve this goal, we investigate the mechanical response of binary mixtures of polystyrene-grafted Fe3O4 (A) and Au (B) nanoparticles by combining coarse-grained molecular dynamics (CG-MD) simulations with a data-driven Gaussian Process (GP) metamodel and Bayesian optimization (BO). We discover NaCl (AB) lattices simultaneously achieve higher modulus and toughness under uniaxial tensile loading due to efficient nanoscale packing and systematically explore their design space by varying grafting parameters and stoichiometry to establish key structure–property relationships. BO provides ∼32% improvement in the toughness-modulus Pareto front, while Sobol sensitivity analysis highlights the dominant influence of larger A PGNs. Remarkably, molecular conformation analysis reveals that smaller B PGNs, although passive in terms of parameter sensitivity, play a critical role in enhancing nanoparticle packing and toughness through effective interparticle entanglements. The results provide a comprehensive framework for understanding and optimizing the mechanics of MC-PGNs, establishing a pathway for navigating the strength–toughness trade-off in polymer nanocomposites.
Pal et al. (Mon,) studied this question.
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