Reduction of thermal conductivity in carbon nanotubes by fullerene encapsulation from machine-learning molecular dynamics simulations

Abstract

The carbon nano-peapod is a representative structure with interlayer van der Waals (vdW) interactions, in which encapsulated fullerene molecules play a critical role in modulating the transport properties of the carbon nanotubes (CNTs). In particular, their influence on the thermal transport characteristics has been the focal point of considerable attention. In this study, we trained an accurate machine learning potential for fullerene-encapsulated CNTs based on the efficient NEP model to investigate their thermal properties. Using equilibrium molecular dynamics simulation along with the spectral decomposition method for thermal conductivity, we find that the thermal conductivity of fullerene-encapsulated CNTs is roughly 55% lower than that of empty CNTs, aligning with experimental observations for CNT bundles with fullerene encapsulation [Kodama et al., Nat. Mater. 16, 892 (2017)]. The research suggests that weak vdW interactions between both the fullerene and CNTs, as well as between fullerene molecules themselves, hinder phonon propagation. The encapsulated fullerene contributes to an increase in phonon scattering within the CNTs, ultimately leading to a reduction in thermal conductivity. We utilized machine learning potential to investigate the structure of fullerene-encapsulated CNTs and their heat transport property. This approach provides valuable insights for performance research of complex systems featuring interlayer vdW interactions.