Research Seminar in Mathematics - Machine Learning for Molecular Dynamics Simulations of Carbon Nanomaterials Growth

Speaker

Andreas Larsson, Institutionen för teknikvetenskap och matematik, Luleå tekniska universitet.

Abstract

Carbon nanotubes (CNTs) have interesting properties that could be used in future nanoelectronic devices. To realize this technological potential, controlled growth of defect-free CNTs is required. Until now, the understanding of atomic-scale growth mechanisms provided by molecular dynamics (MD) simulations has been hampered by their short timescales. Here we present an efficient and accurate machine learning force field for realistic MD simulations of SWCNT growth on iron catalysts [1]. We simulate growth of SWCNTs on near µs timescales, achieving growth of long, defect-free nanotubes and provide new atomic-level insights into the entire growth process. From the evolution of the tube-catalyst interface and importantly the mechanisms behind the formation and healing of defects. Our results highlight the large configurational entropy at the tube-catalyst interface and how defect-free CNTs can grow ultralong if carbon supply and temperature are carefully controlled.

[1] Dynamics of Growing Carbon Nanotube Interfaces Probed by Machine Learning-Enabled Molecular Simulations, D. Hedman, B. McLean, C. Bichara, S. Maruyama, J. A. Larsson, F. Ding, Nat. Commun. 15 (2024) 4076.

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