• 文献标题:   A Qualitative Study of the Disorder Effect on the Phonon Transport in a Two-Dimensional Graphene/h-BN Heterostructure
  • 文献类型:   Article
  • 作  者:   LIU YN, REN WN, AN M, DONG L, GAO L, SHAI XX, WEI TT, NIE LR, HU SQ, ZENG CH
  • 作者关键词:   phonon transport, shift distance, molecular dynamic, disorder, graphene/hbn heterostructure
  • 出版物名称:   FRONTIERS IN MATERIALS
  • ISSN:   2296-8016
  • 通讯作者地址:  
  • 被引频次:   3
  • DOI:   10.3389/fmats.2022.913764
  • 出版年:   2022

▎ 摘  要

Recently, massive efforts have been made to control phonon transport via introducing disorder. Meanwhile, materials informatics, an advanced material-discovery technology that combines data-driven search algorithms and material property simulations, has made significant progress and shown accurate prediction ability in studying the target properties of new materials. However, with the introduction of disorder, the design space of random structures is greatly expanded. Global optimization for the entire domain is nearly impossible with the current computer resource even when materials informatics reduces the design space to a few percent. Toward the goal of reducing design space, we investigate the effect of different types of disorders on phonon transport in two-dimensional graphene/hexagonal boron nitride heterostructure using non-equilibrium molecular dynamics simulation. The simulation results show that when the hexagonal boron nitride is distributed disorderly in the coherent phonon-dominated structure, that is, the structure with a period length of 1 .23 nm, the thermal conductivity is significantly reduced due to the appearance of coherent phonon localization. By qualitatively analyzing different types of disorder, we found that the introduction of disordered structure in the cross direction with a larger shift distance can further reduce the thermal conductivity. Further physical mechanism analysis revealed that the structures with lower thermal conductivity were caused by weak propagation and strong localization of phonon. Our findings have implications for accelerating machine learning in the search for structures with the lowest thermal conductivity, and provide some guidance for the future synthesis of 2D heterostructures with unique thermal properties.