SWCNT Nanotrusses

Description

Data accompanying the research paper: Čanađija, M., Košmerl, V., Zlatić, M., Vrtovšnik, D., Munjas, N.: A computational framework for nanotrusses: input convex neural networks approach, European Journal of Mechanics - A/Solids (2023) Trained neural networks for true-stress vs. true strain uniaxial tension/compression curves and diameter vs. true strain for 818 different single-walled carbon nanotubes at 300 K. Datasets obtained by molecular dynamics (MD) are also enclosed. Results used in the second example in the above paper, obtained by MD are provided. In the case you find this dataset useful, please cite the above paper. Full bibliographic data can be found at the DOI link: https://doi.org/10.1016/j.euromechsol.2023.105195

Steps to reproduce

Files: dataset_uniaxial.csv - uniaxial tension/compression true stress - true strain data for all SWCNT configurations dataset_diameters.csv - diameters vs. true strain for all SWCNT configurations model_and_demo_py.zip - archive with trained neural networks (stress-strain, diameter-strain) and demo Python code ex2_* - files with MD results in the second example in the paper D_initial_NN.csv - initial diameters at 300 K for all SWCNT configurations

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