Although the addition of graphene fillers can transform an electrical insulator to a conductor at a certain threshold of loading ratio, similar transition in thermal conductivity has not been confirmed yet. Here, we use molecular dynamics to investigate if a physical mechanism responsible for thermal percolation exists in a graphene–polymer composite system. We find that when the separation of two graphene flakes falls below 1.8 Å, their interaction transits from van der Waals force to covalent bonding force, which possibly acts as the underlying mechanism for thermal percolation. By constructing primitive graphene networks with different percolation states, we find that under ideal conditions the transition of inter-graphene interaction from van der Waals to covalent bond results in ≈150% increment in the overall thermal conductivity. An analytical model has also been proposed to describe the relation between the effective thermal conductivity of a graphene–polymer composite and the crystallographic orientations of graphene flakes forming the covalent inter-graphene junction. In sum, the formation of an appreciable amount of covalently bonded inter-graphene junctions is the key to take advantage of thermal percolation to significantly improve the thermal conductivity of graphene-reinforced polymer composites.

Efficient microstructure design can strongly accelerate the development of materials. However, the complexity of the microstructure–behavior relation renders the criticalities and degeneracies within the microstructure space highly possible. Criticality means that a slight microstructural change can lead to a dramatic transition in material behavior, while degeneracy means that very different microstructures may lead to similar behaviors. To investigate these microstructural characteristics of the fiber/matrix interface within composite materials, we have proposed a hybrid deep-learning-based framework by integrating the supervised feed-forward neural network and the unsupervised autoencoder, which are trained by the molecular dynamics (MD) simulation results. The well-trained model continuously maps the elemental density images within the interfacial area into a low-dimensional latent space. Assisted by the extracted latent features, we can easily detect the criticalities and degeneracies within the original microstructure space of the composite’s interface. The predicted microstructural criticalities and degeneracies are validated by investigating their atomistic origins through MD simulations. The proposed framework can be employed for the interfacial microstructure design of composite materials by identifying certain interfacial microstructures that might lead to undesirable behaviors.

Due to its ultrahigh in-plane thermal conductivity, graphene nanosheet is expected to significantly improve the thermal conductivity of polymer composites. However, it still lacks clarity that how such improvement is quantitatively influenced by the configuration of the graphene nanosheets. In this work, large-scale molecular dynamics simulations are performed to investigate the effect of size and chemical interconnectivity of the graphene nanosheets on the thermal conductivity of a graphene-reinforced polyamide-6 composite. We find that the thermal conductivity of such a composite can be appreciably improved if all of the graphene nanosheets are covalently bonded together and the average size of the graphene nanosheets is large. Fundamentally, the composite thermal conductivity benefits from more heat taking the path of the graphene architecture and less heat dissipating back into the polymer matrix through the graphene− polymer interface. Analytical modeling indicates that the configuration with largesized graphene nanosheets systematically joined by covalent intergraphene junctions is optimal to attract heat into the graphene architecture and restrain the interfacial heat dissipation, leading to better composite thermal conductivity. Our findings are crucial to understanding the physical mechanism of thermal conductivity enhancement of graphene nanosheets within a polymer matrix, which can be applied to develop highly efficient thermal interface materials.

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The mechanical behavior of composite interface can be influenced by multiple factors, including the morphological roughness, the structure of coating interphase, and the temperature. Here, high-throughput molecular dynamics (MD) simulations are carried out to investigate the entangled effects of these factors on the shear stiffness G, the friction coefficient μ, the debonding strain ϵ_d and stress τ_d, of SiCf/SiC interface. We find that G is maximized by small roughness and high temperature for the optimal chemical bonding effect; μ and ϵ_d are maximized by large roughness and low temperature, taking advantage of the mechanical interlocking effect while avoiding cusp softening; τ_d demonstrates two local maxima which result from the competition between chemical bonding and mechanical interlocking. Provided the MD simulation results, a variational autoencoder (VAE) model is proposed to design the microstructure of SiCf/SiC interface for desired shear properties. According to the validations, the VAE-predicted interfacial configuration demonstrates highly similar shear properties to the reference one, justifying its potential for the microstructure design of composite interface. The results of this work can be employed to facilitate the development of SiCf/SiC composite by taking advantage of the synergistic effects of multiple designable factors.