Tensile tests at room temperature are performed using molecular dynamics on all configurations of single-walled carbon nanotubes up to 4 nm in diameter. Distributions of the Young's modulus, Poisson's ratio, ultimate tensile strength and fracture strain are determined and reported. The results show that the chirality of the nanotube has the greatest influence on the properties. An artificial neural network is developed for the dataset obtained by molecular dynamics and used to predict the mechanical properties. It is clearly shown that Deep Learning provides accurate predictions, with the further advantage that thermal fluctuations are smoothed out. In addition, a through analysis of the effect of dataset size on prediction quality is performed, providing modeling strategies for further researchers.

室温下的拉伸测试是使用分子动力学对直径最大为 4 nm 的单壁碳纳米管的所有配置进行的。测定并报告杨氏模量、泊松比、极限拉伸强度和断裂应变的分布。结果表明，纳米管的手性对其性能影响最大。针对分子动力学获得的数据集开发了人工神经网络，用于预测机械性能。很明显，深度学习提供了准确的预测，进一步的优势是可以消除热波动。此外，通过分析数据集大小对预测质量的影响，为进一步的研究人员提供建模策略。