Machine learning-assisted high-throughput molecular dynamics simulation of high-mechanical performance carbon nanotube structure

Research output: Contribution to journalArticlepeer-review

Abstract

Carbon nanotubes (CNTs) are novel materials with extraordinary mechanical properties. To gain insight on the design of high-mechanical-performance CNT-reinforced composites, the optimal structure of CNTs with high nominal tensile strength was determined in this study, where the nominal values correspond to the cross-sectional area of the entire specimen, including the hollow core. By using machine learning-assisted high-throughput molecular dynamics (HTMD) simulation, the relationship among the following structural parameters/properties was investigated: diameter, number of walls, chirality, and crosslink density. A database, comprising the various tensile test simulation results, was analyzed using a self-organizing map (SOM). It was observed that the influence of crosslink density on the nominal tensile strength tends to gradually decrease from the outside to the inside; generally, the crosslink density between the outermost wall and its adjacent wall is highly significant. In particular, based on our calculation conditions, five-walled, armchair-type CNTs with an outer diameter of 43.39 Å and crosslink densities (between the inner wall and outer wall) of 1.38 ± 1.16%, 1.13 ± 0.69%, 1.54 ± 0.57%, and 1.36 ± 0.35% were believed to be the optimal structure, with the nominal tensile strength and nominal Young’s modulus reaching approximately 58–64 GPa and 677–698 GPa.

Original languageEnglish
Article number2459
Pages (from-to)1-13
Number of pages13
JournalNanomaterials
Volume10
Issue number12
DOIs
Publication statusPublished - 2020 Dec

Keywords

  • Carbon nanotube
  • Frenkel-pair crosslink
  • Machine learning
  • Mechanical properties
  • Molecular dynamics simulations

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Materials Science(all)

Fingerprint Dive into the research topics of 'Machine learning-assisted high-throughput molecular dynamics simulation of high-mechanical performance carbon nanotube structure'. Together they form a unique fingerprint.

Cite this