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.