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Article

Machine Learning-Assisted High-Throughput Molecular Dynamics Simulation of High-Mechanical Performance Carbon Nanotube Structure

1
Department of Aerospace Engineering, Tohoku University, Sendai 980-8579, Japan
2
Institute of Fluid Science, Tohoku University, Sendai 980-8577, Japan
3
School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Korea
*
Author to whom correspondence should be addressed.
Nanomaterials 2020, 10(12), 2459; https://doi.org/10.3390/nano10122459
Received: 6 November 2020 / Revised: 3 December 2020 / Accepted: 7 December 2020 / Published: 9 December 2020
(This article belongs to the Special Issue Nanomechanics of Carbon Nanomaterials)
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. View Full-Text
Keywords: carbon nanotube; molecular dynamics simulations; mechanical properties; Frenkel-pair crosslink; machine learning carbon nanotube; molecular dynamics simulations; mechanical properties; Frenkel-pair crosslink; machine learning
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MDPI and ACS Style

Xiang, Y.; Shimoyama, K.; Shirasu, K.; Yamamoto, G. Machine Learning-Assisted High-Throughput Molecular Dynamics Simulation of High-Mechanical Performance Carbon Nanotube Structure. Nanomaterials 2020, 10, 2459. https://doi.org/10.3390/nano10122459

AMA Style

Xiang Y, Shimoyama K, Shirasu K, Yamamoto G. Machine Learning-Assisted High-Throughput Molecular Dynamics Simulation of High-Mechanical Performance Carbon Nanotube Structure. Nanomaterials. 2020; 10(12):2459. https://doi.org/10.3390/nano10122459

Chicago/Turabian Style

Xiang, Yi, Koji Shimoyama, Keiichi Shirasu, and Go Yamamoto. 2020. "Machine Learning-Assisted High-Throughput Molecular Dynamics Simulation of High-Mechanical Performance Carbon Nanotube Structure" Nanomaterials 10, no. 12: 2459. https://doi.org/10.3390/nano10122459

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