Abstract Mechanical strengthening of composite materials that include carbon nanotubes (CNT) requires strong inter-bonding to achieve significant CNT-CNT or CNT-matrix load transfer. The same principle is applicable to the improvement of CNT bundles and calls for covalent crosslinks between individual tubes. In this work, sulfur crosslinks are studied using a combination of density functional theory (DFT) and classical molecular dynamics (MD). Atomic chains of at least two sulfur atoms or more are shown to be stable between both zigzag and armchair CNTs. All types of crosslinked CNTs exhibit significantly improved load transfer. Moreover, sulfur crosslinks show evidence of a cooperative self-healing mechanism allowing for links to rebond once broken leading to sustained load transfer under shear loading. Additionally, a general approach for utilizing machine learning for assessing the ground state electron density is developed and applied to these sulfur crosslinked CNTs.

中文翻译：

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硫交联碳纳米管中的机器学习电子密度

摘要 包括碳纳米管 (CNT) 的复合材料的机械强化需要强大的相互键合才能实现显着的 CNT-CNT 或 CNT-基质负载转移。相同的原理适用于改进 CNT 束并要求单个管之间的共价交联。在这项工作中，结合使用密度泛函理论 (DFT) 和经典分子动力学 (MD) 来研究硫交联。至少两个或更多硫原子的原子链在锯齿形碳纳米管和扶手椅碳纳米管之间是稳定的。所有类型的交联碳纳米管都表现出显着改善的负载转移。此外，硫交联显示出协同自愈机制的证据，允许链接一旦断裂就重新粘合，从而在剪切载荷下持续传递载荷。此外，