Dynamics of growing carbon nanotube interfaces probed by machine learning-enabled molecular simulations

Daniel Hedman (), Ben McLean, Christophe Bichara, Shigeo Maruyama, J. Andreas Larsson () and Feng Ding ()
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Daniel Hedman: Institute for Basic Science (IBS)
Ben McLean: Institute for Basic Science (IBS)
Christophe Bichara: UMR7325
Shigeo Maruyama: The University of Tokyo
J. Andreas Larsson: Luleå University of Technology
Feng Ding: Institute for Basic Science (IBS)

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Carbon nanotubes (CNTs), hollow cylinders of carbon, hold great promise for advanced technologies, provided their structure remains uniform throughout their length. Their growth takes place at high temperatures across a tube-catalyst interface. Structural defects formed during growth alter CNT properties. These defects are believed to form and heal at the tube-catalyst interface but an understanding of these mechanisms at the atomic-level is lacking. Here we present DeepCNT-22, a machine learning force field (MLFF) to drive molecular dynamics simulations through which we unveil the mechanisms of CNT formation, from nucleation to growth including defect formation and healing. We find the tube-catalyst interface to be highly dynamic, with large fluctuations in the chiral structure of the CNT-edge. This does not support continuous spiral growth as a general mechanism, instead, at these growth conditions, the growing tube edge exhibits significant configurational entropy. We demonstrate that defects form stochastically at the tube-catalyst interface, but under low growth rates and high temperatures, these heal before becoming incorporated in the tube wall, allowing CNTs to grow defect-free to seemingly unlimited lengths. These insights, not readily available through experiments, demonstrate the remarkable power of MLFF-driven simulations and fill long-standing gaps in our understanding of CNT growth mechanisms.

Date: 2024
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DOI: 10.1038/s41467-024-47999-7

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