Fracture strength of Graphene at high temperatures: data driven investigations supported by MD and analytical approaches

The extraordinary opto-electronic and mechanical properties of Graphene makes it a popular material for several applications. However, defects like: cracks, and voids are unavoidable during its production, which can lead to poor properties. Furthermore, the fracture properties degrades at higher temperatures. In this study, the fracture strength of Graphene is investigated as a function of temperature, considering the influence of lattice orientation, initial crack size and its orientation. As a first step, an analytical model is developed to estimate the fracture strength of Graphene with respect to temperature, considering the above parameters. Later on, molecular dynamics simulations are performed with an included initial edge crack in ten different sizes and four orientations, at three particular lattice orientations, and operating at thirteen different temperatures. Finally, a deep machine learning model is developed to estimate the fracture strength of defective Graphene. Results from molecular dynamics simulations are used to train the developed deep machine learning model. Furthermore, the training is enhanced using transfer learning, where the weights and biases for the data set considering $$0^circ$$ lattice orientation are adopted in training the networks for $$13.9^circ$$ and $$30^circ$$ lattice orientations. Results from the developed deep machine learning model are validated by comparing them with the results from the analytical and molecular dynamics models and a good agreement is observed. Thus, a deep machine learning model has been proposed here to estimate the fracture strength of defective Graphene. The developed model serves as a tool for quick estimation fracture strength of defective Graphene.