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

TL;DR

This paper introduces DeepCNT-22, a machine learning force field enabling near-microsecond molecular simulations of defect-free carbon nanotube growth, revealing atomic-scale mechanisms and conditions for ultralong CNT synthesis.

Contribution

The study develops a novel ML force field for simulating CNT growth at unprecedented timescales, providing detailed insights into nucleation, growth, and defect dynamics.

Findings

Atomic-level understanding of CNT nucleation and growth.

Defect formation and healing mechanisms identified.

Conditions for ultralong defect-free CNT growth outlined.

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.