Machine Learning-Assisted High-Throughput Molecular Dynamics Simulation of High-Mechanical Performance Carbon Nanotube Structure
Y Xiang and K Shimoyama and K Shirasu and G Yamamoto, NANOMATERIALS, 10, 2459 (2020).
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 angstrom 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.
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