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Nanomaterials
Volume 10
Issue 12
10.3390/nano10122459
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Article

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

by
Yi Xiang
1,
Koji Shimoyama
2,
Keiichi Shirasu
1 and
Go Yamamoto
1,3,*
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)
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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. 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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