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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2309.05325 |
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| _version_ | 1866929225844391936 |
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| author | Yu, Linfeng Dong, Kexin Yang, Qi Zhang, Yi Zheng, Xiong Wang, Huimin Qin, Zhenzhen Qin, Guangzhao |
| author_facet | Yu, Linfeng Dong, Kexin Yang, Qi Zhang, Yi Zheng, Xiong Wang, Huimin Qin, Zhenzhen Qin, Guangzhao |
| contents | Understanding the fundamental link between structure and functionalization is crucial for the design and optimization of functional materials, since different structural configurations could trigger materials to demonstrate diverse physical, chemical, and electronic properties. However, the correlation between crystal structure and thermal conductivity (\k{appa}) remains enigmatic. In this study, taking two-dimensional (2D) carbon allotropes as study cases, we utilize phonon Boltzmann transport equation (BTE) along with machine learning force constant potential to thoroughly explore the complex folding structure of pure sp2 hybridized carbon materials from the perspective of crystal structure, mode-level phonon resolved thermal transport, and atomic interactions, with the goal of identifying the underlying relationship between 2D geometry and \k{appa}. We propose two potential structure evolution mechanisms for targeted thermal transport properties: in-plane and out-of-plane folding evolutions, which are generally applicable to 2D carbon allotropes. It is revealed that the folded structure produces strong symmetry breaking, and simultaneously produces exceptionally strongly suppressed phonon group velocities, strong phonon-phonon scattering, and weak phonon hydrodynamics, which ultimately lead to low \k{appa}. The insight into the folded effect of atomic structures on thermal transport deepens our understanding of the relationship between structure and functionalization, which offers straightforward guidance for designing novel nanomaterials with targeted \k{appa}, as well as propel developments in materials science and engineering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_05325 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Superfolded configuration induced low thermal conductivity in two-dimensional carbon allotropes revealed via machine learning force constant potential Yu, Linfeng Dong, Kexin Yang, Qi Zhang, Yi Zheng, Xiong Wang, Huimin Qin, Zhenzhen Qin, Guangzhao Materials Science Understanding the fundamental link between structure and functionalization is crucial for the design and optimization of functional materials, since different structural configurations could trigger materials to demonstrate diverse physical, chemical, and electronic properties. However, the correlation between crystal structure and thermal conductivity (\k{appa}) remains enigmatic. In this study, taking two-dimensional (2D) carbon allotropes as study cases, we utilize phonon Boltzmann transport equation (BTE) along with machine learning force constant potential to thoroughly explore the complex folding structure of pure sp2 hybridized carbon materials from the perspective of crystal structure, mode-level phonon resolved thermal transport, and atomic interactions, with the goal of identifying the underlying relationship between 2D geometry and \k{appa}. We propose two potential structure evolution mechanisms for targeted thermal transport properties: in-plane and out-of-plane folding evolutions, which are generally applicable to 2D carbon allotropes. It is revealed that the folded structure produces strong symmetry breaking, and simultaneously produces exceptionally strongly suppressed phonon group velocities, strong phonon-phonon scattering, and weak phonon hydrodynamics, which ultimately lead to low \k{appa}. The insight into the folded effect of atomic structures on thermal transport deepens our understanding of the relationship between structure and functionalization, which offers straightforward guidance for designing novel nanomaterials with targeted \k{appa}, as well as propel developments in materials science and engineering. |
| title | Superfolded configuration induced low thermal conductivity in two-dimensional carbon allotropes revealed via machine learning force constant potential |
| topic | Materials Science |
| url | https://arxiv.org/abs/2309.05325 |