Addressing the exceptional properties of aluminium alloy composites reinforced with graphene, this study presents an interpretable machine learning approach to aid in the rapid and efficient design of such materials. Initially, data on these composites were gathered and optimised in order to create a dataset of composition/process-property. Several machine learning algorithms were used to train various models. The SHAP method was used to interpret and select the best performing model, which happened to be the CatBoost model. The model achieved accurate predictions of hardness and tensile strength, with coefficients of determination of 0.9597 and 0.9882, respectively, and average relative errors of 6.02% and 5.01%, respectively. The results obtained from the SHAP method unveiled the correlation between the composition, process and properties of aluminium alloy composites reinforced with graphene. By comparing the predicted and experimental data in this study, all machine learning models exhibited prediction errors within 10%, confirming their ability to generalise. This study offers valuable insights and support for designing high-performance aluminium matrix composites reinforced with graphene and showcases the implementation of machine learning in materials science.

Graphene has become an ideal reinforcement for reinforced metal matrix composites due to its excellent mechanical properties. However, the theory of graphene reinforcement in graphene/aluminum matrix composites is not yet well developed. In this paper, the effect of different temperatures on the mechanical properties of the metal matrix is investigated using a classical molecular dynamics approach, and the effects of the configuration and distribution of graphene in the metal matrix on the mechanical properties of the composites are also described in detail. It is shown that in the case of a monolayer graphene-reinforced aluminum matrix, the simulated stretching process does not break the graphene as the strain increases, but rather, the graphene and the aluminum matrix have a shearing behavior, and thus, the graphene “pulls out" from the aluminum matrix. In the parallel stretching direction, the tensile stress tends to increase with the increase of the graphene area ratio. In the vertical stretching direction, the tensile stress tends to decrease as the percentage of graphene area increases. In the parallel stretching direction, the tensile stress of the system tends to decrease as the angle between graphene and the stretching direction increases. It is important to investigate the effect of a different graphene distribution in the aluminum matrix on the mechanical properties of the composites for the design of high-strength graphene/metal matrix composites.

Silicon carbide (SiC) is a promising material for thermoelectric power generation. The characterization of thermal transport properties is essential to understanding their applications in thermoelectric devices. The existence of stacking faults, which originate from the “wrong” stacking sequences of Si–C bilayers, is a general feature of SiC. However, the effects of stacking faults on the thermal properties of SiC are not well understood. In this study, we evaluated the accuracy of Tersoff, MEAM, and GW potentials in describing the thermal transport of SiC. Additionally, the thermal conductivity of 3C/4H-SiC nanowires was investigated using non-equilibrium molecular dynamics simulations (NEMD). Our results show that thermal conductivity exhibits an increase and then saturation as the total lengths of the 3C/4H-SiC nanowires vary from 23.9 nm to 95.6 nm, showing the size effect of molecular dynamics simulations of the thermal conductivity. There is a minimum thermal conductivity, as a function of uniform period length, of the 3C/4H-SiC nanowires. However, the thermal conductivities of nanowires weakly depend on the gradient period lengths and the ratio of 3C/4H. Additionally, the thermal conductivity of 3C/4H-SiC nanowires decreases continuously from compressive strain to tensile strain. The reduction in thermal conductivity suggests that 3C/4H-SiC nanowires have potential applications in advanced thermoelectric devices. Our study provides insights into the thermal transport properties of SiC nanowires and can guide the development of SiC-based thermoelectric materials.