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Special Issue:
SPECIAL TOPIC — Artificial intelligence and smart materials innovation: From fundamentals to applications
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| SPECIAL TOPIC — Artificial intelligence and smart materials innovation: From fundamentals to applications |
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Unveiling the thermal transport mechanisms in novel carbon-based graphene-like materials using machine-learning potential |
| Yao-Yuan Zhang(章耀元)1, Meng-Qiu Long(龙孟秋)2,†, Sai-Jie Cheng(程赛杰)3, and Wu-Xing Zhou(周五星)3,‡ |
1 Dundee International Institute, Central South University, Changsha 410083, China;
2 Hunan Key Laboratory of Super Micro-structure and Ultrafast Process, Central South University, Changsha 410083, China;
3 School of Materials Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China |
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Abstract This study presents a systematic investigation of thermal transport properties in a novel class of carbon-based graphene-like materials (AKCs). Through first-principles calculations combined with the phonon Boltzmann transport equation and machine-learning potential, we analyzed the lattice thermal conductivity and its microscopic mechanisms in three structures: AKC60, AKC33, and AKC41. The research reveals that these materials exhibit significant in-plane thermal conductivity at room temperature (191.0 W/m$\cdot$K, 122.6 W/m$\cdot$K, and 248.3 W/m$\cdot$K, respectively), though an order of magnitude lower than that of graphene. Through detailed analysis of phonon dispersion relations, group velocities, three-phonon scattering phase space, and Grüneisen parameters, we uncovered the physical origins of AKCs' relatively lower thermal conductivity. The findings indicate that despite AKC60's larger primitive cell, its better preservation of graphene's honeycomb structure leads to superior harmonic properties, resulting in higher thermal conductivity than that of AKC33 with its smaller primitive cell. These discoveries provide valuable guidance for AKCs' applications in future electronic devices.
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Received: 26 January 2025
Revised: 31 March 2025
Accepted manuscript online: 08 April 2025
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PACS:
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71.15.-m
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(Methods of electronic structure calculations)
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51.20.+d
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(Viscosity, diffusion, and thermal conductivity)
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68.90.+g
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(Other topics in structure, and nonelectronic properties of surfaces and interfaces; thin films and low-dimensional structures)
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| Fund: This work was supported by the National Natural Science Foundation of China (Grant No. 12074115) and the Science and Technology Innovation Program of Hunan Province (Grant No. 2023RC3176). |
Corresponding Authors:
Meng-Qiu Long, Wu-Xing Zhou
E-mail: mqlong@csu.edu.cn;wuxingzhou@hnu.edu.cn
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Cite this article:
Yao-Yuan Zhang(章耀元), Meng-Qiu Long(龙孟秋), Sai-Jie Cheng(程赛杰), and Wu-Xing Zhou(周五星) Unveiling the thermal transport mechanisms in novel carbon-based graphene-like materials using machine-learning potential 2025 Chin. Phys. B 34 067101
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