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• W3203233640 abstract "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." @default.
• W3203233640 created "2021-10-11" @default.
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• W3203233640 date "2021-06-21" @default.
• W3203233640 modified "2023-09-23" @default.
• W3203233640 title "Self-healing mechanism of lithium in lithium metal batteries" @default.
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• W3203233640 hasPublicationYear "2021" @default.
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