Journal
ADVANCED SCIENCE
Volume 9, Issue 12, Pages -Publisher
WILEY
DOI: 10.1002/advs.202105574
Keywords
dendrite growth; dendrite morphology; Li deposition; molecular dynamic simulation; neural network; potential; self-healing
Categories
Funding
- Starting Fund of Peking University, Shenzhen Graduate School
- Fujian Science & Technology Innovation Laboratory for Energy Devices of China [21C-LAB]
- National Natural Science Foundation of China [12174162]
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Li is an ideal anode material for advanced batteries, but dendrite growth limits its commercial application. By using machine learning to construct a Li potential model, research shows that self-healing mechanisms can effectively promote dendrite-free Li formation.
Li 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, machine learning is used to construct a Li potential model with quantum-mechanical computational accuracy. Molecular dynamics simulations in this study with this model reveal two self-healing mechanisms in a large Li-metal system, viz. surface self-healing, and bulk self-healing. It is concluded that self-healing occurs rapidly in nanoscale; thus, minimizing the voids between the Li grains using several comprehensive methods can effectively facilitate the formation of dendrite-free Li.
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