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Classical molecular dynamics (MD) simulations have been performed toelucidate the mechanism of single-walled carbon nanotube (SWCNT) growth.However, discussing the chirality has been challenging due to topologicaldefects in simulated SWCNTs. Recently, the application of neural network tointeratomic potentials has been actively studied and such interatomicpotentials are called neural network potentials (NNPs). NNPs have a betterability for approximate functions and can predict complex systems' energy moreaccurately than conventional interatomic potentials. In this study, wedeveloped an NNP to describe SWCNT growth more accurately. We successfullydemonstrated defect-free and chirality-definable growth of SWCNTs on Fenanoparticles by MD simulation. Furthermore, it was revealed that edgevacancies of SWCNTs caused defect formation and adatom diffusion contributed tovacancy healing, preventing defect formation.

Catalyzed Single-Walled Carbon Nanotube Growth by Machine Learning Molecular Dynamics Simulation