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Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach
Author(s) -
Furmanchuk Al'ona,
Saal James E.,
Doak Jeff W.,
Olson Gregory B.,
Choudhary Alok,
Agrawal Ankit
Publication year - 2018
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.25067
Subject(s) - seebeck coefficient , stoichiometry , thermoelectric effect , materials science , thermoelectric materials , crystallite , field (mathematics) , range (aeronautics) , computer science , thermodynamics , mathematics , chemistry , physics , metallurgy , composite material , pure mathematics
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.