标题： 基于机器学习势场的单壁碳纳米管的手性依赖动力学 显示英文标题

标题： Chirality-Dependent Kinetics of Single-Walled Carbon Nanotubes from Machine-Learning Force Fields

摘要： 单壁碳纳米管（SWCNTs）手性起源一直存在长期争议。 由机器学习力场（MLFF）驱动的分子动力学（MD）模拟，可以在接近从头算精度下研究界面动力学，为揭示SWCNTs的形成机制提供了一种强大技术。 在这里，我们开发了一种钴-碳MLFF，并在钴催化剂上进行生长模拟，以研究在气-液-固（VLS）条件下SWCNTs生长的手性偏好。 通过微动力学建模，我们再现了观察到的生长和缺陷动力学，证明了它们对手性依赖的关系。 观察到，虽然初始手性分配可能与纳米管帽的构型简并有关，但五边形缺陷在成核后立即形成并被解决。 这些过程，我们称之为直径控制机制，不仅将直径控制到最佳状态，还显著改变了手性分布。 因此，我们的工作为SWCNTs的手性依赖动力学提供了一个微动力学建模流程，突显了缺陷动力学在手性起源中的重要作用。 显示英文摘要

摘要： The origin of the chirality of single-walled carbon nanotubes (SWCNTs) has been a long-standing dispute. Molecular dynamics (MD) simulations driven by machine-learning force fields (MLFF), which can study the interface dynamics under near ab-initio accuracy, provides a powerful technique to reveal the formation mechanism of SWCNTs. Here, we develop a cobalt-carbon MLFF and perform growth simulations on a cobalt catalyst to investigate the chirality preference of the growth of SWCNTs under the vapor-liquid-solid (VLS) regime. Through microkinetic modeling, we reproduce the observed growth and defect kinetics, demonstrating their dependence on the chirality. It is observed that while the initial chirality assignment is likely related to the configurational degeneracy of the nanotube caps, pentagon defects immediately form and resolve after nucleation. Such processes, which we name as diameter control mechanisms, not only control the diameter toward an optimum but also shift the chirality distribution drastically. Our work therefore offers a microkinetic modeling workflow for the chirality-dependent kinetics of the SWCNTs, highlighting the important contribution of the defect kinetics to the chirality origination.