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arXiv · 2302.09542

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

Daniel Hedman·Ben McLean·Christophe Bichara·Shigeo Maruyama·J. Andreas Larsson·Feng Ding
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Abstract

Carbon nanotubes (CNTs) are currently considered a successor to silicon in future nanoelectronic devices. To realize this, controlled growth of defect-free nanotubes is required. Until now, the understanding of atomic-scale CNT growth mechanisms provided by molecular dynamics simulations has been hampered by their short timescales. Here, we develop an efficient and accurate machine learning force field, DeepCNT-22, to simulate the complete growth of defect-free single-walled CNTs (SWCNTs) on iron catalysts at near-microsecond timescales. We provide atomic-level insight into the nucleation and growth processes of SWCNTs, including the evolution of the tube-catalyst interface and the mechanisms underlying defect formation and healing. Our simulations highlight the maximization of SWCNT-edge configurational entropy during growth and how defect-free CNTs can grow ultralong if carbon supply and temperature are carefully controlled.

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Daniel Hedman, Ben McLean, Christophe Bichara, Shigeo Maruyama, J. Andreas Larsson, Feng Ding. 2023-02-19. Dynamics of growing carbon nanotube interfaces probed by machine learning-enabled molecular simulations. https://arxiv.org/abs/2302.09542

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