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

Abstract: Given the high potential for applications utilizing the unique properties of single-walled carbon nanotubes (SWCNTs), there is considerable enthusiasm for addressing the challenges associated with synthesizing SWCNTs with specific chirality. To elucidate the mechanisms that determine the chirality of SWCNTs during growth, intensive efforts have been devoted to classical molecular dynamics (MD) simulations. However, the mechanism of chirality determination has not been fully clarified, which can partly be attributed to the limited accuracy of empirical interatomic potentials in reproducing the behavior of carbon and metal atoms. In this work, we develop a neural network potential (NNP) for carbon-metal system to accurately describe the SWCNT growth, and perform MD simulations of SWCNT growth using the NNP. The MD simulations illustrate the defect-free, chirality-definable growth of SWCNTs, highlighting the dynamic rearrangement of edge configurations and the consistency between the probability of edge configuration appearance and the entropy-driven edge stability model proposed here. It is also shown that the edge defect formation is induced by vacancy and suppressed by vacancy healing through adatom diffusion on the SWCNT edges. These results provide insights into the edge formation thermodynamics and kinetics of SWCNTs, an important clue to the chirality-controlled synthesis of SWCNTs.