Phonon Thermal Transport in Bi2Te3 from a Deep-Neural-Network Interatomic Potential
P Zhang and ZH Zhang and Y Liu and ZY Wang and ZH Lu and R Xiong, PHYSICAL REVIEW APPLIED, 18, 054022 (2022).
Bi2Te3 is a widely used thermoelectric material with strong anharmonicity. Determination of its ther-mal conductivity requires consideration of the high-order phonon scattering process, which makes it extremely time consuming and challenging to accurately calculate its thermal conductivity by obtaining high-order force constants based on density-functional theory. In this work, a deep-neural-network poten- tial is developed to reproduce phonon dispersion and predict the lattice thermal conductivity of Bi2Te3. The equilibrium molecular dynamics simulations combined with this potential are performed to calculate the lattice thermal conductivity and the results nicely match the experimental values. Meanwhile, we find the generalized gradient approximation with the DFT-D3 functional can accurately reproduce the experi-mental lattice constants of Bi2Te3 and provide a description of the phonon dispersion in Bi2Te3 as well as the local density approximation. Furthermore, we explore the influence of the native point defects on ther-mal conductivity, and find that Te vacancies have the most significant effect on the reduction of thermal conductivity, owing to the appreciable inhibition of phonon propagation speed by Te vacancies, and the additional scattering among original low-frequency optical phonons and the fresh low-frequency optical phonons moving downward from high frequency region, which provides some theoretical guidance for reducing thermal conductivity in experimental research.
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