Electronic transport descriptors for the rapid screening of thermoelectric materials
Tianqi Deng, Jose Recatala-Gomez, Masato Ohnishi, D. V. Maheshwar, Repaka, Pawan Kumar, Ady Suwardi, Anas Abutaha, Iris Nandhakumar, Kanishka, Biswas, Michael B. Sullivan, Gang Wu, Junichiro Shiomi, Shuo-Wang Yang, Kedar, Hippalgaonkar

TL;DR
This study uses data-driven screening and first-principles calculations to identify promising thermoelectric materials, revealing the importance of ionised impurity scattering and dielectric properties in optimizing thermoelectric performance.
Contribution
It introduces ground-state transport descriptors and highlights dielectric constant as a key factor for thermoelectric efficiency, advancing material screening methods.
Findings
Carrier scattering dominated by ionised impurities and polar optical phonons.
Predicted high-performance thermoelectrics with zT above 1 at 500 K.
High dielectric constant correlates with improved carrier mobility.
Abstract
The discovery of novel materials for thermoelectric energy conversion has potential to be accelerated by data-driven screening combined with high-throughput calculations. One way to increase the efficacy of successfully choosing a candidate material is through its evaluation using transport descriptors. Using a data-driven screening, we selected 12 potential candidates in the trigonal ABX2 family, followed by charge transport property simulations from first principles. The results suggest that carrier scattering processes in these materials are dominated by ionised impurities and polar optical phonons, contrary to the oft-assumed acoustic-phonon-dominated scattering. Combined with calculations of thermal conductivity based on three-phonon scattering, we predict p-type AgBiS2 and TlBiTe2 as potential high-performance thermoelectrics in the intermediate temperature range for low grade…
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Taxonomy
TopicsAdvanced Thermoelectric Materials and Devices · Machine Learning in Materials Science · 2D Materials and Applications
