  Li metal is an ideal anode material for use in state-of-the-art secondary
batteries. However, Li-dendrite growth is a safety concern and results in low
coulombic efficiency, which significantly restricts the commercial application
of Li secondary batteries. Unfortunately, the Li deposition (growth) mechanism
is poorly understood on the atomic scale. Here, we used machine learning to
construct a Li potential model with quantum-mechanical computational accuracy.
Molecular dynamics simulations in this study with this model revealed two
self-healing mechanisms in a large Li-metal system, viz. surface self-healing
and bulk self-healing, and identified three Li-dendrite morphologies under
different conditions, viz. "needle", "mushroom", and "hemisphere". Finally, we
introduce the concepts of local current density and variance in local current
density to supplement the critical current density when evaluating the
probability of self-healing.
