Efficient prediction of mechanical properties of Boron Nitride Nanosheets using artificial neural networks

Abstract

This paper aims to evaluate the mechanical properties of Boron Nitride Nanosheets (BNNS) and their vital use in nano-electromechanical systems (NEMS). By employing molecular Dynamics (MD) simulation, modelling of the atomic structure was done. The mechanical response of BNNS under various parameters (strain rate, temperature, chirality, dimension) enabled the generation of a comprehensive data set that accurately represents their elastic properties. The dataset obtained from MD simulation was subsequently utilized to construct an artificial neural network (ANN) model, tailored to forecast the Young’s modulus of BNNS accurately. This work aimed to improve the model's efficiency by refining the design of ANN, which significantly reduces the computational time while maintaining higher accuracy predictions. The findings demonstrate precise and rapid forecasts for developing components based on BNNS in NEMS. This paper establishes an analogy between in-depth atomistic simulations and real-world engineering applications presenting a new approach for accurately predicting the properties of nanomaterials.