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Chin. Phys. B, 2023, Vol. 32(5): 054402    DOI: 10.1088/1674-1056/acb9e6
Special Issue: SPECIAL TOPIC — Smart design of materials and design of smart materials
SPECIAL TOPIC—Smart design of materials and design of smart materials Prev   Next  

Thermal transport properties of two-dimensional boron dichalcogenides from a first-principles and machine learning approach

Zhanjun Qiu(邱占均)1, Yanxiao Hu(胡晏箫)1, Ding Li(李顶)1, Tao Hu(胡涛)1, Hong Xiao(肖红)1, Chunbao Feng(冯春宝)1,2,†, and Dengfeng Li(李登峰)1,2,‡
1 School of Science, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2 Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Abstract  The investigation of thermal transport is crucial to the thermal management of modern electronic devices. To obtain the thermal conductivity through solution of the Boltzmann transport equation, calculation of the anharmonic interatomic force constants has a high computational cost based on the current method of single-point density functional theory force calculation. The recent suggested machine learning interatomic potentials (MLIPs) method can avoid these huge computational demands. In this work, we study the thermal conductivity of two-dimensional MoS$_{2}$-like hexagonal boron dichalcogenides (H-B$_{2}{VI}_{2}$; ${VI} = {\rm S}$, Se, Te) with a combination of MLIPs and the phonon Boltzmann transport equation. The room-temperature thermal conductivity of H-B$_{2}$S$_{2}$ can reach up to 336 W$\cdot $m$^{-1}\cdot $K$^{-1}$, obviously larger than that of H-B$_{2}$Se$_{2}$ and H-B$_{2}$Te$_{2}$. This is mainly due to the difference in phonon group velocity. By substituting the different chalcogen elements in the second sublayer, H-B$_{2}{VI}{VI}^\prime $ have lower thermal conductivity than H-B$_{2}{VI}_{2}$. The room-temperature thermal conductivity of B$_{2}$STe is only 11% of that of H-B$_{2}$S$_{2}$. This can be explained by comparing phonon group velocity and phonon relaxation time. The MLIP method is proved to be an efficient method for studying the thermal conductivity of materials, and H-B$_{2}$S$_{2}$-based nanodevices have excellent thermal conduction.
Keywords:  boron dichalcogenides      thermal conductivity      machine learning interatomic potentials      first-principles calculation  
Received:  07 December 2022      Revised:  02 February 2023      Accepted manuscript online:  08 February 2023
PACS:  44.10.+i (Heat conduction) (Phonon-phonon interactions)  
  05.60.Gg (Quantum transport)  
  65.90.+i (Other topics in thermal properties of condensed matter)  
Fund: Project supported by Scientific and Technological Research of Chongqing Municipal Education Commission (Grant No. KJZD-K202100602) and the funding of Institute for Advanced Sciences of Chongqing University of Posts and Telecommunications (Grant No. E011A2022326).
Corresponding Authors:  Chunbao Feng, Dengfeng Li     E-mail:;

Cite this article: 

Zhanjun Qiu(邱占均), Yanxiao Hu(胡晏箫), Ding Li(李顶), Tao Hu(胡涛), Hong Xiao(肖红),Chunbao Feng(冯春宝), and Dengfeng Li(李登峰) Thermal transport properties of two-dimensional boron dichalcogenides from a first-principles and machine learning approach 2023 Chin. Phys. B 32 054402

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