Thermal Stability and Mechanical Properties of Hollow Si Nanowires from Atomic Modeling Combined with a Machine-Learning Prediction for Application as Li-Ion Battery Anodes

Atomic modeling within the classical mechanics formalism is performed to investigate the thermal ability as well as mechanical properties of Si hollow nanowires, and Young's modulus of the Si hollow nanowires is further predicted by combining molecular dynamics simulations with machine learning. The modeling at the atomic scale provides the effect of temperature, the hollow nanowires' cross-sectional outer and inner radius, and applied tension loading on their thermal ability and deformation behaviors. The simulation results reveal that the inner or outer radius as well as temperature and applied loading significantly affect packing structural evolution of the nanowires, and there exist evolutions from the hollow nanostructures to solid nanowires through inner wall collapse in certain temperature or applied strain ranges. The Lode-Nadai value distributions in the Si hollow nanowires provide insights into the loading states of the atoms during tension. The atomic hydrostatic pressures are used to identify stress-transfer paths during the elasticity, plasticity, and fracture stages of the Si hollow nanowires. The neural network-based analysis identifies that Young's modulus values of the hollow nanowires significantly depend on the cross-sectional size of the nanowires and predicts the critical size of the cross section for the nanowires that shows there are significant changes for Young's modulus.