Obtaining the mechanical properties of boron nitride nanosheets (BNNSs) requires extensive computational atomistic simulations, so it is necessary to predict to reduce time costs. In this work, we obtained the ultimate tensile strength and Young’s modulus of the BNNS material through molecular dynamics (MDs) simulations by taking into account factors, such as the BNNSs’ chirality, layer number, ambient temperature, and strain rate. Subsequently, employing comprehensive training and optimization of the MDs data, we developed multiple ML models to estimate the ultimate tensile strength and Young’s modulus. Among these models, the random forest model was chosen for its accurate prediction of the mechanical properties of the BNNSs, offering significant benefits for performance analysis and the engineering design of two-dimensional nanomaterials resembling BNNSs. Finally, based on the predicted results of the ML models, we propose a predictive model for the mechanical properties of the BNNSs, which serves as a valuable reference for future research endeavors.

The slip mechanism between the chunk and wafer during high-speed dynamic scanning of the extreme ultraviolet lithography (EUV) motion stage remains unclear. Considering real-machined roughness, molecular dynamics (MD) simulations were performed to investigate the nanotribological behavior of 6H-SiC sliders on single-crystal silicon substrates. The effects of sinusoidal asperity parameters and normal loads on wear and slip were systematically analyzed. Results indicate that, for friction between sinusoidal asperities and ideal flat surfaces, the amplitude of surface parameters exhibits negligible influence on friction. In contrast, reduced normal loads and lower periods significantly increase both friction force and coefficient of friction (COF).