Prediction of phonon properties of cubic boron nitride with vacancy defects and isotopic disorders by using a neural network potential

被引:0
作者
Zhang, Jingwen [1 ]
Zhang, Junjie [1 ]
Bao, Guoqiang [1 ]
Li, Zehan [1 ]
Li, Xiaobo [1 ]
Liu, Te-Huan [1 ]
Yang, Ronggui [1 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Wuhan 430074, Hubei, Peoples R China
基金
中国国家自然科学基金;
关键词
THERMAL-CONDUCTIVITY; POINT-DEFECTS; FILMS; GROWTH; TRANSPORT; TEMPERATURE; DEPOSITION; ULTRAHARD; DIAMOND; ENERGY;
D O I
10.1063/5.0198431
中图分类号
O59 [应用物理学];
学科分类号
摘要
Cubic boron nitride (c-BN) is a promising ultra-wide bandgap semiconductor for high-power electronic devices. Its thermal conductivity can be substantially modified by controlling the isotope abundance and by the quality of a single crystal. Consequently, an understanding of the phonon transport in c-BN crystals, with both vacancy defects and isotopic disorders at near-ambient temperatures, is of practical importance. In the present study, a neural network potential (NNP) for c-BN has been developed, which has facilitated the investigation of phonon properties under these circumstances. As a result, the phonon dispersion and the three- and four-phonon scattering rates that were predicted with this NNP were in close agreement with those obtained from density-functional theory (DFT) calculations. The thermal conductivities of the c-BN crystals were also investigated, with boron (B) vacancies ranging from 0.0% to 0.6%, by using equilibrium molecular dynamics simulations based on the Green-Kubo formula. These simulations accurately capture vacancy-induced phonon softening, localized vibration modes, and phonon localization effects. As has previously been experimentally prepared, four isotope-modified c-BN samples were selected for analyses in the evaluation of the impact of isotopic disorders. The calculated thermal conductivities aligned well with the DFT benchmarks. In addition, the present study was extended to include a c-BN crystal with a natural abundance of B atoms, which also contained B vacancies. Reasonable thermal conductivities and vibrational characteristics, within the temperature range of 250-500 K, were then obtained.
引用
收藏
页数:7
相关论文
共 70 条
  • [1] Machine-learning-based interatomic potential for phonon transport in perfect crystalline Si and crystalline Si with vacancies
    Banaei, Hasan
    Guo, Ruiqiang
    Hashemi, Amirreza
    Lee, Sangyeop
    [J]. PHYSICAL REVIEW MATERIALS, 2019, 3 (07)
  • [2] Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
    Bartok, Albert P.
    Payne, Mike C.
    Kondor, Risi
    Csanyi, Gabor
    [J]. PHYSICAL REVIEW LETTERS, 2010, 104 (13)
  • [3] Generalized neural-network representation of high-dimensional potential-energy surfaces
    Behler, Joerg
    Parrinello, Michele
    [J]. PHYSICAL REVIEW LETTERS, 2007, 98 (14)
  • [4] Deposition of thick cubic boron nitride films: The route to practical applications
    Bello, I
    Chan, CY
    Zhang, WJ
    Chong, YM
    Leung, KM
    Lee, ST
    Lifshitz, Y
    [J]. DIAMOND AND RELATED MATERIALS, 2005, 14 (3-7) : 1154 - 1162
  • [5] PROJECTOR AUGMENTED-WAVE METHOD
    BLOCHL, PE
    [J]. PHYSICAL REVIEW B, 1994, 50 (24): : 17953 - 17979
  • [6] First principles studies of point defects and impurities in cubic boron nitride
    Castineira, JLP
    Leite, JR
    Scolfaro, LMR
    Enderlein, R
    Alves, JLA
    Alves, HWL
    [J]. MATERIALS SCIENCE AND ENGINEERING B-SOLID STATE MATERIALS FOR ADVANCED TECHNOLOGY, 1998, 51 (1-3): : 53 - 57
  • [7] Lattice thermal transport in superhard hexagonal diamond and wurtzite boron nitride: A comparative study with cubic diamond and cubic boron nitride
    Chakraborty, Pranay
    Xiong, Guoping
    Cao, Lei
    Wang, Yan
    [J]. CARBON, 2018, 139 : 85 - 93
  • [8] Ultrahigh thermal conductivity in isotope-enriched cubic boron nitride
    Chen, Ke
    Song, Bai
    Ravichandran, Navaneetha K.
    Zheng, Qiye
    Chen, Xi
    Lee, Hwijong
    Sun, Haoran
    Li, Sheng
    Gamage, Geethal Amila Udalamatta Gamage
    Tian, Fei
    Ding, Zhiwei
    Song, Qichen
    Rai, Akash
    Wu, Hanlin
    Koirala, Pawan
    Schmidt, Aaron J.
    Watanabe, Kenji
    Lv, Bing
    Ren, Zhifeng
    Shi, Li
    Cahill, David G.
    Taniguchi, Takashi
    Broido, David
    Chen, Gang
    [J]. SCIENCE, 2020, 367 (6477) : 555 - +
  • [9] Machine learning of accurate energy-conserving molecular force fields
    Chmiela, Stefan
    Tkatchenko, Alexandre
    Sauceda, Huziel E.
    Poltavsky, Igor
    Schuett, Kristof T.
    Mueller, Klaus-Robert
    [J]. SCIENCE ADVANCES, 2017, 3 (05):
  • [10] Accelerated computation of lattice thermal conductivity using neural network interatomic potentials
    Choi, Jeong Min
    Lee, Kyeongpung
    Kim, Sangtae
    Moon, Minseok
    Jeong, Wonseok
    Han, Seungwu
    [J]. COMPUTATIONAL MATERIALS SCIENCE, 2022, 211