Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach.
J Comput Chem
; 39(4): 191-202, 2018 Feb 05.
Article
em En
| MEDLINE
| ID: mdl-28960343
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.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
J Comput Chem
Assunto da revista:
QUIMICA
Ano de publicação:
2018
Tipo de documento:
Article