Screening Promising Thermoelectric Materials in Binary Chalcogenides through High-Throughput Computations
Tiantian Jia, Zhenzhen Feng, Shuping Guo, Xuemei Zhang, Yongsheng, Zhang

TL;DR
This study develops efficient computational descriptors to rapidly screen and identify promising thermoelectric materials within binary chalcogenides, successfully predicting new candidates and validating known ones.
Contribution
The paper introduces new high-throughput computational descriptors (hi and mma) for screening thermoelectric materials, reducing computational cost and enabling large-scale discovery.
Findings
Screened 243 binary chalcogenides for thermoelectric potential.
Predicted 50 promising thermoelectric materials, including 23 novel candidates.
Validated descriptors by identifying known thermoelectric materials.
Abstract
The high-throughput (HT) computational method is a useful tool to screen high performance functional materials. In this work, using the deformation potential method under the single band model, we evaluate the carrier relaxation time and establish an electrical descriptor (\c{hi}) characterized by the carrier effective masses based on the simple rigid band approximation. The descriptor (\c{hi}) can be used to reasonably represent the maximum power factor without solving the electron Boltzmann transport equation. Additionally, the Gr\"uneisen parameter ({\gamma}), a descriptor of the lattice anharmonicity and lattice thermal conductivity, is efficiently evaluated using the elastic properties, omitting the costly phonon calculations. Applying two descriptors (\c{hi} and {\gamma}) to binary chalcogenides, we HT compute 243 semiconductors and screen 50 promising thermoelectric materials.…
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Taxonomy
TopicsAdvanced Thermoelectric Materials and Devices · Machine Learning in Materials Science · 2D Materials and Applications
