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Given the high potential for applications utilizing the unique properties ofsingle-walled carbon nanotubes (SWCNTs), there is considerable enthusiasm foraddressing the challenges associated with synthesizing SWCNTs with specificchirality. To elucidate the mechanisms that determine the chirality of SWCNTsduring growth, intensive efforts have been devoted to classical moleculardynamics (MD) simulations. However, the mechanism of chirality determinationhas not been fully clarified, which can partly be attributed to the limitedaccuracy of empirical interatomic potentials in reproducing the behavior ofcarbon and metal atoms. In this work, we develop a neural network potential(NNP) for carbon-metal system to accurately describe the SWCNT growth, andperform MD simulations of SWCNT growth using the NNP. The MD simulationsillustrate the defect-free, chirality-definable growth of SWCNTs, highlightingthe dynamic rearrangement of edge configurations and the consistency betweenthe probability of edge configuration appearance and the entropy-driven edgestability model proposed here. It is also shown that the edge defect formationis induced by vacancy and suppressed by vacancy healing through adatomdiffusion on the SWCNT edges. These results provide insights into the edgeformation thermodynamics and kinetics of SWCNTs, an important clue to thechirality-controlled synthesis of SWCNTs.

Edge Dynamics in Iron-Cluster Catalyzed Growth of Single-Walled Carbon  Nanotubes Revealed by Molecular Dynamics Simulations based on a Neural  Network Potential