Chirality-Dependent Kinetics of Single-Walled Carbon Nanotubes from Machine-Learning Force Fields

Area of Science:

• Materials Science
• Computational Chemistry
• Nanotechnology

Background:

• The origin of chirality in single-walled carbon nanotubes (SWCNTs) remains a significant scientific debate.
• Understanding SWCNT formation is crucial for tailoring their properties for advanced applications.

Purpose of the Study:

• To investigate the chirality preference during SWCNT growth using advanced computational methods.
• To elucidate the role of catalyst-mediated processes and defect kinetics in determining SWCNT chirality.

Main Methods:

• Development of a novel cobalt-carbon machine-learning force field (MLFF).
• Execution of molecular dynamics (MD) simulations under the vapor-liquid-solid (VLS) growth regime.
• Application of microkinetic modeling to analyze growth and defect kinetics.

Main Results:

• Simulations successfully reproduced observed growth and defect kinetics, showing chirality dependence.
• Identified 'diameter control mechanisms' involving pentagon defect formation and resolution post-nucleation.
• Demonstrated a significant shift in chirality distribution due to defect kinetics, favoring the experimentally observed (6,5) SWCNT.

Conclusions:

• Defect kinetics play a critical role in the origination and preferential formation of specific SWCNT chiralities.
• The developed MLFF and microkinetic modeling workflow provide a powerful tool for studying chirality-dependent phenomena in SWCNTs.
• This research offers key insights into controlling SWCNT chirality for targeted applications.