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

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

*Corresponding author for this work

Research output: Contribution to journalArticle

17 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)191-202
Number of pages12
JournalJournal of computational chemistry
Volume39
Issue number4
DOIs
StatePublished - Feb 5 2018

Keywords

  • Seebeck coefficient
  • data mining
  • nonstoichiometric materials
  • prediction
  • thermoelectric properties
  • web application

ASJC Scopus subject areas

  • Chemistry(all)
  • Computational Mathematics

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