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arXiv · 2512.12940

Predicting the Thermal Conductivity Collapse in SWCNT Bundles: The Interplay of Symmetry Breaking and Scattering Revealed by Machine-Learning-Driven Quantum Transport

Feng Tao·Xiaoliang Zhang·Dawei Tang·Shigeo Maruyama·Ya Feng
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Abstract

We combine machine learning (ML)-based neuroevolution potentials (NEP) with anharmonic lattice dynamics and the Boltzmann transport equation (ALD-BTE) to achieve a quantitative and mode-resolved description of thermal transport in individual (10, 0) zigzag single-walled carbon nanotubes (SWCNTs) and their bundles. Our analysis reveals a dual mechanism behind the drastic suppression of thermal conductivity in bundles: first, the breaking of rotational symmetry in isolated SWCNTs dramatically enhances the scattering rates of symmetry-sensitive phonon modes, such as the twist (TW) mode. Second, the emergence of new inter-tube phonon modes introduces abundant additional scattering channels across the entire frequency spectrum. Crucially, the incorporation of quantum Bose-Einstein (BE) statistics is essential to accurately capture these phenomena, enabling our approach to quantitatively reproduce experimental observations. This work establishes the combination of ML-driven interatomic potentials and ALD-BTE as a predictive framework for nanoscale thermal transport, effectively bridging the gap between theoretical models and experimental measurements.

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Feng Tao, Xiaoliang Zhang, Dawei Tang, Shigeo Maruyama, Ya Feng. 2025-12-15. Predicting the Thermal Conductivity Collapse in SWCNT Bundles: The Interplay of Symmetry Breaking and Scattering Revealed by Machine-Learning-Driven Quantum Transport. https://doi.org/10.1021/acs.nanolett.5c06521

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