| SPECIAL TOPIC — Heat conduction and its related interdisciplinary areas |
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Thermal conductivity of carbon nanotubes using nonequilibrium molecular dynamics combined with a machine learning potential |
| Jia-Hua Liu(刘嘉华)1, Shuo Cui(崔硕)1, Feng Guo(郭峰)1,†, Yu-Shi Wen(文玉史)2, Chun-Liang Ji(纪春亮)3, and Xiao-Chun Wang(王晓春)1 |
1 School of Physical Science and Information Technology, Liaocheng University, Liaocheng 252000, China;
2 Institute of Chemical Materials, China Academy of Engineering Physics (CAEP), Mianyang 621900, China;
3 School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China |
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Abstract Large-scale and long-time-span nonequilibrium molecular dynamics simulations have been performed to determine the thermal conductivity of single-walled and double-walled carbon nanotubes (CNTs) using a machine learning potential trained on atomic energies and forces from density functional theory calculations for sp2-hybridized carbon. The size dependence of graphene and CNTs up to 1 μm has been studied with 200000 atoms and simulation times up to 5 ns. The simulations reveal that thermal transport, whether ballistic, quasi-ballistic, or diffusive, is determined by the relationship between the sample length and the effective mean-free path (MFP). The system size has less effect on thermal conductivity when the sample length significantly exceeds the MFP. Radial tensile strain in CNTs causes the C–C bond length to increase in smaller-diameter CNTs, resulting in a phonon softening effect that subsequently reduces thermal conductivity. An analytical function is proposed to describe the relationship between phonon relaxation time and nanotube diameter. The thermal conductivity of the double-walled CNT is lower than that of an equivalent-size single-walled CNT. Phonon–phonon scattering, interlayer van der Waals interactions, and degenerate coupling of transverse acoustic modes are considered to contribute to the reduction in thermal transport.
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Received: 28 October 2025
Revised: 06 January 2026
Accepted manuscript online: 07 January 2026
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PACS:
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63.20.K-
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(Phonon interactions)
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82.20.Wt
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(Computational modeling; simulation)
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63.22.Rc
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(Phonons in graphene)
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51.20.+d
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(Viscosity, diffusion, and thermal conductivity)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11504153 and 12002325). |
Corresponding Authors:
Feng Guo
E-mail: gfeng.alan@foxmail.com
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Cite this article:
Jia-Hua Liu(刘嘉华), Shuo Cui(崔硕), Feng Guo(郭峰), Yu-Shi Wen(文玉史), Chun-Liang Ji(纪春亮), and Xiao-Chun Wang(王晓春) Thermal conductivity of carbon nanotubes using nonequilibrium molecular dynamics combined with a machine learning potential 2026 Chin. Phys. B 35 036302
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