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

Researchers have developed a machine learning force field, DeepCNT-22, to simulate the growth of carbon nanotubes (CNTs) on iron catalysts. These simulations reveal the detailed atomic-level processes of CNT growth, including nucleation, defect formation, and healing. The study found that CNT edges exhibit significant configurational entropy, and that defects formed during growth can be healed under certain conditions. This approach provides valuable insights into achieving defect-free CNT growth, which is crucial for advanced technological applications.

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