Data-driven discovery of 3D and 2D thermoelectric materials
- 26 June 2020
- journal article
- research article
- Published by IOP Publishing in Journal of Physics: Condensed Matter
- Vol. 32 (47), 475501
- https://doi.org/10.1088/1361-648x/aba06b
Abstract
In this work, we first 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 and then train machine-learning models on the thermoelectric data. Out of 36000 three-dimensional and 900 two-dimensional materials currently in the publicly available JARVIS-DFT database, we identify 2932 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 and power factors. We compute the Seebeck coefficients for all the materials currently in the database and validate our calculations by 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 and power 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: https://www.ctcms.nist.gov/~knc6/JVASP.html , https://www.ctcms.nist.gov/jarvisml/ and https://jarvis.nist.gov/ .Keywords
This publication has 62 references indexed in Scilit:
- Chemical accuracy for the van der Waals density functionalJournal of Physics: Condensed Matter, 2009
- Thermoelectric properties of Tl‐doped Bi2Se3 single crystalsCrystal Research and Technology, 2009
- Cooling, Heating, Generating Power, and Recovering Waste Heat with Thermoelectric SystemsScience, 2008
- New Directions for Low‐Dimensional Thermoelectric MaterialsAdvanced Materials, 2007
- Thermoelectric Properties of Doped Half-Heuslers NbCoSn1-xSbx and Nb0.99Ti0.01CoSn1-xSbxJapanese Journal of Applied Physics, 2006
- BoltzTraP. A code for calculating band-structure dependent quantitiesComputer Physics Communications, 2006
- Thermoelectric Materials for Space and Automotive Power GenerationMRS Bulletin, 2006
- Thermal analysis and synthesis from the melts of Cu-based quaternary compounds Cu–III–IV–VI4 and Cu2–II–IV–VI4 (II=Zn,Cd; III=Ga,In; IV=Ge,Sn; VI=Se)Journal of Crystal Growth, 2000
- Generalized Gradient Approximation Made SimplePhysical Review Letters, 1996
- Efficient iterative schemes forab initiototal-energy calculations using a plane-wave basis setPhysical Review B, 1996