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Data-driven Discovery of 3D and 2D Thermoelectric Materials



Kamal Choudhary, Kevin Garrity, Francesca Tavazza


Abstract: In this work, we perform a systematic search for high-efficiency, three-dimensional (3D) and two-dimensional (2D) thermoelectric materials by combining semiclassical transport techniques with density functional theory (DFT) calculations. Out of 30000 three-dimensional and 900 two-dimensional materials currently in the publicly available JARVIS-DFT database, we identify 698 3D and 148 2D promising thermoelectric materials using a multi-steps screening procedure, where specific thresholds are chosen for key quantities like bandgaps, Seebeck- coefficients, power factors and zT factors. We computed the Seebeck-coefficients for all the materials currently in the database and validated our calculations comparing our results, for a subset of materials, to experimental and existing computational datasets. We also investigate the effect of chemical, structural, crystallographic and dimensionality trends on thermoelectric performance. We predict several classes of efficient 3D and 2D materials such as Ba(MgX)2 (X=P,As,Bi), X2YZ6 (X=K,Rb, Y=Pd,Pt, Z=Cl,Br), K2PtX2(X=S,Se), NbCu3X4 (X=S,Se,Te), Sr2XYO6 (X=Ta, Zn, Y=Ga, Mo), TaCu3X4 (X=S, Se,Te), and XYN (X=Ti, Zr, Y=Cl, Br). Finally, as high-throughput DFT is computationally expensive, we train machine learning models using gradient boosting decision trees (GBDT) and classical force-field inspired descriptors (CFID) for n-and p-type Seebeck coefficients, power factors and zT factors, to quickly pre-screen materials for guiding the next set of DFT calculations. The dataset and tools are made publicly available at the websites: and .
Journal of Physics: Condensed Matter


thermoelectric, DFT, 2D, transport


Choudhary, K. , Garrity, K. and Tavazza, F. (2018), Data-driven Discovery of 3D and 2D Thermoelectric Materials, Journal of Physics: Condensed Matter, [online], (Accessed April 14, 2024)
Created June 25, 2018, Updated October 12, 2021