Probing the stochastic fracture behavior of twisted bilayer graphene: efficient ANN based molecular dynamics simulations for complete probabilistic characterization

The present article outlines a probabilistic investigation of the uniaxial tensile behaviour of twisted bilayer graphene (tBLG) structures. In this regard, the twist angle (θ) and temperature (T) are considered as the control parameters and the ultimate tensile strength (UTS) and failure strain of the tBLG structures are considered as the responses. It is observed that with the increase in twist angle (θ) of tBLG; the fracture responses exhibit a declining trend deterministically. The tBLG twisted with the magic angle (θ = 1.08o) results in around 7% decrease in UTS and nearly 24% decrease in failure strain, when compared with normal BLG (θ = 0̊). The Monte Carlo simulation (MCS) based random sampling is performed for the considered control parameters, wherein θ is varied from 0o to 30o, and temperature is varied from 100 K to 900 K. Within such bounds of the input parameters, the training (64 samples) and validation (8 samples) sample spaces are constructed. In the next step, molecular dynamics (MD) simulation of uniaxial tensile deformation of the modelled tBLG structures is carried out for each instance of the sample space. The dataset is subsequently used to form and validate the artificial neural network (ANN). The computationally efficient machine learning (ML) model is further utilized to perform the detailed investigation of fracture behaviour of the tBLG structures in the probabilistic framework. Such analysis captures all the possible instances of variation in the input parameters and leads to deep insights in the material behaviour, which would have otherwise remained unnoticed due to the prohibitive nature of conducting a large number of MD simulations. The novelty of the present study lies in the probabilistic interpretation of the tensile behaviour of tBLG structures subjected to variation in twist angle (θ) and temperature (T). The preparation of nano-scale samples with the exact design specifications such as twist angle is often extremely difficult, which leads to inevitable stochastic system disorders. The current article essentially proposes a probabilistic avenue of quantifying the effect of such disorders on the failure properties of tBLG.