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Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach

Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A... The regression model‐based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. We introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient. To address off‐stoichiometry in materials, the predictive tool is trained on a mix of stoichiometric and nonstoichiometric materials. The tool is implemented into a web application (http://info.eecs.northwestern.edu/SeebeckCoefficientPredictor) to assist field scientists in the discovery of novel thermoelectric materials. © 2017 Wiley Periodicals, Inc. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Computational Chemistry Wiley

Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach

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References (58)

Publisher
Wiley
Copyright
© 2018 Wiley Periodicals, Inc.
ISSN
0192-8651
eISSN
1096-987X
DOI
10.1002/jcc.25067
Publisher site
See Article on Publisher Site

Abstract

The regression model‐based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. We introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient. To address off‐stoichiometry in materials, the predictive tool is trained on a mix of stoichiometric and nonstoichiometric materials. The tool is implemented into a web application (http://info.eecs.northwestern.edu/SeebeckCoefficientPredictor) to assist field scientists in the discovery of novel thermoelectric materials. © 2017 Wiley Periodicals, Inc.

Journal

Journal of Computational ChemistryWiley

Published: Jan 5, 2018

Keywords: ; ; ; ; ;

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