Versatilely Manipulating the Mechanical Properties of Polymer Nanocomposites by Incorporating Porous Fillers: A Molecular Dynamics Simulation Study.

Polymer nanocomposites (PNCs) have been attracting myriad scientific and technological attention due to their promising mechanical and functional properties. However, there remains a need for an efficient method that can further strengthen the mechanical performance of PNCs. Here, we propose a strategy to design and fabricate novel PNCs by incorporating porous fillers (PFs) such as metal-organic frameworks with ultrahigh specific surface areas and tunable nanospaces to polymer matrices via coarse-grained molecular dynamics simulations. Three important parameters─the polymer chain stiffness (), the interaction strength between the PF center and the end functional groups of polymer chains (ε), and the PF weight fraction ()─are systematically examined. First, attributed to the penetration of polymer chains into PFs at a strong ε, the dimension of polymer chains such as the radius of gyration and the end-to-end distance increases greatly as a function of compared to the case of the neat polymer system. The penetration of polymer chains is validated by characterizing the radial distribution function between end functional groups and filler centers, as well as the visualization of the snapshots. Also, the dispersion state of PFs tends to be good because of the chain penetration. Then, the glass transition temperature ratio of PNCs to that of the neat systems exhibits a maximum in the case of = 5ε, indicating that the strongest interlocking between polymer chains and PFs occurs at intermediate chain stiffness. The polymer chain dynamics of PNCs decreases to a plateau at = 5ε and then becomes stable, and the relative mobility to that of the neat system as well presents the same variation trend. Furthermore, the mechanical property under uniaxial deformation is thoroughly studied, and intermediates , ε, and can bring about the best mechanical property. This is because of the robust penetration and interaction, which is confirmed by calculating the stress of every component of PNCs with and without end functional groups and PF centers as well as the nonbonded interaction energy change between different components. Finally, the optimal condition ( = 5.36ε, ε = 5.29ε, and = 6.54%) to design the PNC with superior mechanical behavior is predicted by Gaussian process regression, an active machine learning (ML) method. Overall, incorporating PFs greatly enhances the entanglements and interactions between polymer chains and nanofillers and brings effective mechanical reinforcements with lower filler weight fractions. We anticipate that this will provide new routes to the design of mechanically reinforced PNCs.