Edge Dynamics in Iron-Cluster Catalyzed Growth of Single-Walled Carbon Nanotubes Revealed by Molecular Dynamics Simulations based on a Neural Network Potential
Ikuma Kohata, Ryo Yoshikawa, Kaoru Hisama, Christophe Bichara, Keigo Otsuka, Shigeo Maruyama

TL;DR
This study uses neural network-enhanced molecular dynamics simulations to explore how edge dynamics influence the chirality and defect formation in the growth of single-walled carbon nanotubes, offering insights for controlled synthesis.
Contribution
It introduces a neural network potential for accurate simulation of carbon-metal systems during SWCNT growth, revealing mechanisms of edge rearrangement and defect formation.
Findings
Edge configurations follow an entropy-driven stability model.
Vacancy formation induces edge defects, which are healed by adatom diffusion.
Simulations show defect-free, chirality-definable SWCNT growth.
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…
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