
pmid: 35212469
pmc: PMC9036043
AbstractLi 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.
Dendrite (mathematics), Alternative medicine, potential, Electrode, Self-healing, Deposition (geology), Lithium metal, Lithium (medication), Engineering, Endocrinology, dendrite growth, Materials Chemistry, Pathology, Nanotechnology, Anode Materials, Research Articles, Metal, Physics, Q, Mechanism (biology), Geology, Chemistry, Physical chemistry, Lithium-ion Battery Technology, Faraday efficiency, Physical Sciences, Metallurgy, Dendrite-Free Deposition, Medicine, Li deposition, Composite material, Nanoscopic scale, neural network, Science, Chemical physics, Materials Science, Geometry, Quantum mechanics, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Electrical and Electronic Engineering, Lithium Battery Technologies, FOS: Nanotechnology, molecular dynamic simulation, Accelerating Materials Innovation through Informatics, Paleontology, FOS: Earth and related environmental sciences, Materials science, Anode, dendrite morphology, Sediment, Mathematics
Dendrite (mathematics), Alternative medicine, potential, Electrode, Self-healing, Deposition (geology), Lithium metal, Lithium (medication), Engineering, Endocrinology, dendrite growth, Materials Chemistry, Pathology, Nanotechnology, Anode Materials, Research Articles, Metal, Physics, Q, Mechanism (biology), Geology, Chemistry, Physical chemistry, Lithium-ion Battery Technology, Faraday efficiency, Physical Sciences, Metallurgy, Dendrite-Free Deposition, Medicine, Li deposition, Composite material, Nanoscopic scale, neural network, Science, Chemical physics, Materials Science, Geometry, Quantum mechanics, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Electrical and Electronic Engineering, Lithium Battery Technologies, FOS: Nanotechnology, molecular dynamic simulation, Accelerating Materials Innovation through Informatics, Paleontology, FOS: Earth and related environmental sciences, Materials science, Anode, dendrite morphology, Sediment, Mathematics
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