  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/ .
