Electrical conductivity is one of several outstanding features of graphene–polymer nanocomposites, but calculations of this property require the intricate features of the underlying conduction processes to be accounted for. To this end, a novel Monte Carlo method was developed. We first established a randomly distributed graphene nanoplatelet (GNP) network. Then, based on the tunneling effect, the contact conductance between the GNPs was calculated. Coated surfaces (CSs) were next set up to calculate the current flow from the GNPs to the polymer. Using the equipotential approximation, the potentials of the GNPs and CSs met Kirchhoff’s current law, and, based on Laplace equation, the potential of the CSs was obtained from the potential of the GNP by the walk-on-spheres (WoS) method. As such, the potentials of all GNPs were obtained, and the electrical conductivity of the GNP polymer composites was calculated. The barrier heights, polymer conductivity, diameter and thickness of the GNP determining the electrical conductivity of composites were studied in this model. The calculated conductivity and percolation threshold were shown to agree with experimental data.

Graphene agglomeration tends to develop with the increase of graphene loading. In this article, we present a unified model that first considers the evolution of graphene agglomeration and then incorporates it into the calculation of electrical and mechanical properties of agglomerated graphene/polymer nanocomposites. In the evolution of graphene agglomerates, a modified nanoparticle distance in terms of yield strength is introduced, while in the calculation of composite properties, a two‐scale framework that consists of the graphene‐rich agglomerates and the remainder as the graphene‐poor region is constructed. In electrical conduction, electron tunneling is modeled through Simmons formula and its difference with the widely used Cauchy function is compared. We highlight that both Simmons and Cauchy functions could well describe the interfacial tunneling activity, but the former is physics‐based while the latter is statistics‐based. In the calculation of nonlinear elastoplastic response, a field‐fluctuation method is adopted. We also demonstrated how the presented model gives rise to experimentally consistent electrical and mechanical properties of graphene/polypropylene nanocomposites, and how filler agglomeration hinders the performance of the materials.