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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.12940 |
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| _version_ | 1866911514042040320 |
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| author | Tao, Feng Zhang, Xiaoliang Tang, Dawei Maruyama, Shigeo Feng, Ya |
| author_facet | Tao, Feng Zhang, Xiaoliang Tang, Dawei Maruyama, Shigeo Feng, Ya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12940 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting the Thermal Conductivity Collapse in SWCNT Bundles: The Interplay of Symmetry Breaking and Scattering Revealed by Machine-Learning-Driven Quantum Transport Tao, Feng Zhang, Xiaoliang Tang, Dawei Maruyama, Shigeo Feng, Ya Mesoscale and Nanoscale Physics Computational Physics 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. |
| title | Predicting the Thermal Conductivity Collapse in SWCNT Bundles: The Interplay of Symmetry Breaking and Scattering Revealed by Machine-Learning-Driven Quantum Transport |
| topic | Mesoscale and Nanoscale Physics Computational Physics |
| url | https://arxiv.org/abs/2512.12940 |