English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this work, we first perform a systematic search for high-efficiencythree-dimensional (3D) and two-dimensional (2D) thermoelectric materials bycombining semiclassical transport techniques with density functional theory(DFT) calculations and then train machine-learning models on the thermoelectricdata. Out of 36000 three-dimensional and 900 two-dimensional materialscurrently in the publicly available JARVIS-DFT database, we identify 2932 3Dand 148 2D promising thermoelectric materials using a multi-steps screeningprocedure, where specific thresholds are chosen for key quantities likebandgaps, Seebeck coefficients and power factors. We compute the Seebeckcoefficients for all the materials currently in the database and validate ourcalculations by comparing our results, for a subset of materials, toexperimental and existing computational datasets. We also investigate theeffect of chemical, structural, crystallographic and dimensionality trends onthermoelectric performance. We predict several classes of efficient 3D and 2Dmaterials 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 iscomputationally expensive, we train machine learning models using gradientboosting decision trees (GBDT) and classical force-field inspired descriptors(CFID) for n-and p-type Seebeck coefficients and power factors, to quicklypre-screen materials for guiding the next set of DFT calculations. The datasetand 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/ .

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