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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 27, 2017
- Source ID
- 10.1002/jcc.25067
Entities
People
- Alok Choudhary
- Al’ona Furmanchuk
- Ankit Agrawal
- Gregory B. Olson
- James E Saal
- Jeff W. Doak
Organizations
- Air Force Office of Scientific Research
- Defense Advanced Research Projects Agency
- National Institute of Standards and Technology
- National Science Foundation
- Naval Information Warfare Systems Command
- Northwestern University
- United States Department of Energy