  Understanding the mechanical properties of solid-state materials at the
atomic scale is crucial for developing novel materials. For example, amorphous
LiSi alloys are attractive anode materials for solid-state Li-ion batteries but
face mechanical instabilities due to significant volume variations with
changing Li content. A fundamental grasp of the mechanical behavior in such
systems is essential to address their poor mechanical integrity. Experimental
methods offer insufficient information to elaborate on dynamic mechanical
degradation mechanisms at the atomic scale, and computationally demanding
first-principles methods, like DFT, struggle to access the system sizes needed
for modeling mechanical phenomena. Machine learning potentials (MLPs) can
overcome the computational constraints of traditional DFT-based simulations,
enabling large-scale, accurate, and efficient simulations. Here, we provide a
concise tutorial on developing and applying MLPs to investigate mechanical
properties in materials systems, ranging from bulk to nanoparticles, using
Li-Si alloys as an example. Trained on a comprehensive dataset (~45,000 DFT
structures) with the aenet package accelerated by PyTorch, a robust MLP is
constructed to reproduce results consistent with previous experimental
observations. We demonstrate applying the MLP to realistic structures to
visualize the deformation mechanism and determine the origin of mechanical
instabilities caused by fracturing. This work aims to establish MLP-based
simulations as a tool to understand the atomic-scale mechanical behavior in
different materials systems.
