Gradient nano-grained graphene as 2D thermal rectifier: A molecular dynamics based machine learning study
文献类型：期刊论文
| 作者 | Xu, Ke7; Liang, Ting1,8; Fu, Yuequn7; Wang, Zhen2; Fan, Zheyong3; Wei, Ning5; Xu, Jianbin1,8; Zhang, Zhisen7; Wu, Jianyang4,7 |
| 刊名 | APPLIED PHYSICS LETTERS
 |
| 出版日期 | 2022 |
| 卷号 | 121期号:13页码:133501 |
| ISSN号 | 0003-6951 |
| DOI | 10.1063/5.0108746 |
| 英文摘要 | Machine learning has become an excellent tool for scientists and engineers to predict, design, and fabricate next-generation material. Here, we report the thermal conductivity and thermal rectification of gradient-nano-grained graphene (GNGG) by molecular dynamic simulation with machine learning. It is revealed that the thermal conductivity of GNGG is mainly determined by the average grain size, while its thermal rectification factor varies linearly with the gradient of nanograins. Deep neural network-based machine learning models are developed to estimate the thermal transport properties of GNGG using microstructural signatures, such as the location, number, and orientation of 5|7 pairs. The results stress the pivotal roles of 5|7 defects in the planar thermal transports of graphene and indicate that high-performance 2D thermal rectifiers for heat flow control and energy harvesting can be achieved by bio-inspired gradient structure engineering. The findings are expected to supply a theoretical strategy for the design of bio-inspired materials and create a method to predict the potential properties of the material candidates by using machine learning, which can save the abundant expense of developing the material by using the classical method. Published under an exclusive license by AIP Publishing. |
| 学科主题 | Physics |
| 语种 | 英语 |
| 源URL | [http://ir.itp.ac.cn/handle/311006/27768]  |
| 专题 | 理论物理研究所_理论物理所1978-2010年知识产出 |
| 作者单位 | 1.Chinese Univ Hong Kong, Dept Elect Engn & Mat Sci, Shatin,NT, Hong Kong 999077, Peoples R China 2.Chinese Univ Hong Kong, Technol Res Ctr, Shatin,NT, Hong Kong 999077, Peoples R China 3.Chinese Acad Sci, Inst Theoret Phys, Beijing 100190, Peoples R China 4.Jiangnan Univ, Jiangsu Key Lab Adv Food Mfg Equipment & Technol, Wuxi 214122, Peoples R China 5.Bohai Univ, Coll Phys Sci & Technol, Jinzhou 121013, Peoples R China 6.Norwegian Univ Sci & Technol NTNU, NTNU Nanomech Lab, N-7491 Trondheim, Norway 7.Xiamen Univ, Res Inst Biomimet & Soft Matter, Jiujiang Res Inst, Dept Phys, Xiamen 361005, Peoples R China 8.Xiamen Univ, Fujian Prov Key Lab Soft Funct Mat Res, Xiamen 361005, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Ke,Liang, Ting,Fu, Yuequn,et al. Gradient nano-grained graphene as 2D thermal rectifier: A molecular dynamics based machine learning study[J]. APPLIED PHYSICS LETTERS,2022,121(13):133501. |
| APA | Xu, Ke.,Liang, Ting.,Fu, Yuequn.,Wang, Zhen.,Fan, Zheyong.,...&Wu, Jianyang.(2022).Gradient nano-grained graphene as 2D thermal rectifier: A molecular dynamics based machine learning study.APPLIED PHYSICS LETTERS,121(13),133501. |
| MLA | Xu, Ke,et al."Gradient nano-grained graphene as 2D thermal rectifier: A molecular dynamics based machine learning study".APPLIED PHYSICS LETTERS 121.13(2022):133501. |
入库方式： OAI收割
来源：理论物理研究所
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