Machine learning for predicting ultralow thermal conductivity and high ZT in complex thermoelectric materials
Yuzhou Hao, Yuting Zuo, Jiongzhi Zheng, Wenjie Hou, Hong Gu, Xiaoying, Wang, Xuejie Li, Jun Sun, Xiangdong Ding, Zhibin Gao

TL;DR
This study combines machine learning and thermal transport theory to accurately predict ultralow thermal conductivity and high ZT in Tl$_9$SbTe$_6$, revealing mechanisms behind its exceptional thermoelectric performance.
Contribution
It introduces a machine learning-assisted approach to analyze thermal transport mechanisms in complex thermoelectric materials, achieving precise predictions of thermal conductivity and ZT.
Findings
Thermal conductivity of 0.31 W/m·K at room temperature closely matches experiments.
Maximum ZT of 3.17 in p-type and 2.26 in n-type Tl$_9$SbTe$_6$ at specific temperatures.
Off-diagonal heat flux terms significantly influence lattice thermal conductivity.
Abstract
Efficient and precise calculations of thermal transport properties and figure of merit, alongside a deep comprehension of thermal transport mechanisms, are essential for the practical utilization of advanced thermoelectric materials. In this study, we explore the microscopic processes governing thermal transport in the distinguished crystalline material TlSbTe by integrating a unified thermal transport theory with machine learning-assisted self-consistent phonon calculations. Leveraging machine learning potentials, we expedite the analysis of phonon energy shifts, higher-order scattering mechanisms, and thermal conductivity arising from various contributing factors like population and coherence channels. Our finding unveils an exceptionally low thermal conductivity of 0.31 W m K at room temperature, a result that closely correlates with experimental observations.…
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Taxonomy
TopicsAdvanced Thermoelectric Materials and Devices · Machine Learning in Materials Science · Thermal properties of materials
