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

5 years ago

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

Al'ona Furmanchuk, Jeff W. Doak, James E. Saal, Alok Choudhary, Gregory B. Olson, Ankit Agrawal

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. The machine learning is employed for prediction of the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. To address off-stoichiometry in experimental samples, new descriptive features are introduced. 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.

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1002/jcc.25067