1 Data-driven Discovery of 3D and 2D Thermoelectric Materials Kamal Choudhary, Kevin F. Garrity and Francesca Tavazza Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA. 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/ . Corresponding author: Kamal Choudhary (E-mail: [email protected])
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Data-driven Discovery of 3D and 2D Thermoelectric Materials
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Data-driven Discovery of 3D and 2D Thermoelectric Materials
Kamal Choudhary, Kevin F. Garrity and Francesca Tavazza
Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA.
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
the cell, radial distribution peak at 7.5 ร , 9.4 ร and 9.5 ร , first-neighbor based angular distribution
peak at 178 degree, mean of product of polarizability and atomic mass, ratio of atomic radii and
molar volume and refractive index of individual constituent elements43.
Fig. 6 Feature importance distribution plot for the classification models.
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IV CONCLUSIONS
In summary, we use semiclassical transport methods based on density functional theory
calculations to evaluate the thermoelectric properties of both bulk (3D) and monolayer (2D)
materials. In addition to identifying interesting candidate materials, we also show chemical,
crystallographic, compositional and dimensionality trends for the whole dataset. We screen 2D
materials and evaluate trends between the thermoelectric performance of bulk and monolayer
geometries. We identify several compositional classes with high thermoelectric performance. We
predict ultra-low lattice thermal conductivity in the ZrBrN class of materials. Although the
constant-relaxation time approximation is a crude approximation, it allows the generation of large-
scale database for initial screening of thermoelectric materials. Finally, we train machine learning
models to accelerate the future screening processes. We believe that our data and tools for
evaluating and predicting thermoelectric performance will greatly enhance the discovery and
characterization of thermoelectric materials.
V SUPPLEMENTARY MATERIAL
See the supplementary material for the comparison of theoretical and experimental data as well as
the dataset generated in the present work.
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