Abstract
A novel computational approach is proposed to investigate the shear modulus of graphene nanostructures. In this approach, the factors that affect the shear modulus of graphene structures are analysed using an integrated artificial intelligence (AI) cluster comprising molecular dynamics (MD) and gene expression programming. The MD-based-AI approach has the ability to formulate the explicit relationship of shear modulus graphene nanostructure with respect to aspect ratio, temperature, number of atomic planes and vacancy defects. In addition, the shear modulus of graphene predicted using an integrated MD-based-AI model is in good agreement with that of experimental results obtained from the literature. The sensitivity and parametric analysis were further conducted to find out specific influence and variation of each of the input system parameters on the shear modulus of two graphene structures. It was found that the number of defects has the most dominating influence on the shear modulus of graphene nanostructure.
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