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

Abstract Carbon nanotubes (CNTs), hollow cylinders of carbon 1 with diameters in the nanometer range, hold great promise for advanced technologies 2–5 , provided their structure is controlled and remains uniform throughout their length 6–9 . Their growth, facilitated by a metal catalyst, takes place at high temperatures across a tube-catalyst interface comprising a few tens of carbon atoms. During growth, the structure, and properties of CNTs are defined but defects can alter them 10 . These defects are believed to form and heal at the tube-catalyst interface although an understanding of these mechanisms at the atomic-level is still lacking 11, 12 . Here, using molecular dynamics simulations driven by a machine learning force field 13 (MLFF) we developed, DeepCNT-22, 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 during growth, with large fluctuations in the chiral structure of the CNT-edge. This contradicts the previous notion of a continuous spiral growth mode 14 , but confirms that the growing tube edge exhibits significant configurational entropy 15 . We demonstrate that defects form stochastically at the tube-catalyst interface, however, under low growth rates and high temperatures, healing becomes more efficient than formation, allowing CNTs to grow defect-free to seemingly unlimited lengths. These insights, not readily available via experiments, demonstrate the remarkable power of MLFF-driven simulations and fill long-standing gaps in our understanding of CNT growth mechanisms.