Abnormal thermal conductivity increase in β-Ga2O3 by an unconventional bonding mechanism using machine-learning potential

被引:7
作者
Zhou, Wu-Xing [1 ,2 ]
Wu, Cheng-Wei [1 ,2 ]
Cao, Hao-Ran [1 ,2 ]
Zeng, Yu-Jia [1 ,2 ]
Xie, Guofeng [1 ,2 ]
Zhang, Gang [3 ]
机构
[1] Hunan Univ Sci & Technol, Sch Mat Sci & Engn, Xiangtan 411201, Peoples R China
[2] Hunan Univ Sci & Technol, Hunan Prov Key Lab Adv Mat New Energy Storage & Co, Xiangtan 411201, Peoples R China
[3] ASTAR, Inst High Performance Comp, Singapore 138632, Singapore
基金
中国国家自然科学基金;
关键词
Thermal conductivity; Machine-learning potential; Boltzmann transport equation; beta-Ga2O3; THERMOELECTRIC PERFORMANCE; TRANSPORT;
D O I
10.1016/j.mtphys.2025.101677
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
/3-Ga2O3, with its ultrawide band gap (-4.9 eV) and high critical electric field, holds potential in power electronics but is limited by low thermal conductivity, which is critical to the performance and reliability because the high level of heat flux density induced by the extremely high levels of power density. Combining first-principles calculations, machine-learning potentials, and solving the phonon Boltzmann transport equation, we found that substituting octahedral-coordinated Ga with Al significantly enhances thermal conductivity from 100K to 800K. At room temperature, Al-substituted beta-Ga2O3 achieves 38.91 W/mK, more than 2-fold that of pristine /3-Ga2O3 (17.10 W/mK) and even higher than /3-Al2O3 (30.52 W/mK). This enhancement, unusual due to the heavier atomic mass and mixed mass distribution, is rooted in suppressed anharmonic characteristics caused by reduced bonding strength inhomogeneity. Our results may inspire the rational design of materials with tailored thermal properties through chemical bonding mechanisms.
引用
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页数:8
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