Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach
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.
- Authors:
-
- Northwestern University, Evanston, IL (United States)
- QuesTeck Innovations LLC, Evanston, IL (United States)
- Publication Date:
- Research Org.:
- Northwestern Univ., Evanston, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); National Institute of Standards and Technology (NIST); Defense Advanced Research Project Agency (DARPA); SPAWAR Systems Center Pacific (SSC Pacific); US Air Force Office of Scientific Research (AFOSR); National Science Foundation (NSF)
- OSTI Identifier:
- 1537575
- Alternate Identifier(s):
- OSTI ID: 1395185
- Grant/Contract Number:
- SC0007456; SC0014330; N66001-15-C-4036; 70NANB14H012; FA9550-12-1-0458; CCF-1409601; DE‐SC0007456, DE‐SC0014330
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Computational Chemistry
- Additional Journal Information:
- Journal Volume: 39; Journal Issue: 4; Journal ID: ISSN 0192-8651
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
Furmanchuk, Al'ona, Saal, James E., Doak, Jeff W., Olson, Gregory B., Choudhary, Alok, and Agrawal, Ankit. Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach. United States: N. p., 2017.
Web. doi:10.1002/jcc.25067.
Furmanchuk, Al'ona, Saal, James E., Doak, Jeff W., Olson, Gregory B., Choudhary, Alok, & Agrawal, Ankit. Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach. United States. https://doi.org/10.1002/jcc.25067
Furmanchuk, Al'ona, Saal, James E., Doak, Jeff W., Olson, Gregory B., Choudhary, Alok, and Agrawal, Ankit. Wed .
"Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach". United States. https://doi.org/10.1002/jcc.25067. https://www.osti.gov/servlets/purl/1537575.
@article{osti_1537575,
title = {Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach},
author = {Furmanchuk, Al'ona and Saal, James E. and Doak, Jeff W. and Olson, Gregory B. and Choudhary, Alok and Agrawal, Ankit},
abstractNote = {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.},
doi = {10.1002/jcc.25067},
journal = {Journal of Computational Chemistry},
number = 4,
volume = 39,
place = {United States},
year = {Wed Sep 27 00:00:00 EDT 2017},
month = {Wed Sep 27 00:00:00 EDT 2017}
}
Web of Science
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