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This content will become publicly available on September 27, 2018

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

Authors:
ORCiD logo [1] ;  [2] ;  [2] ;  [2] ;  [3] ;  [3]
  1. Institute for Public Health and Medicine, Feinberg School of Medicine, Center for Health Information Partnerships, Northwestern University, Chicago Illinois 60611
  2. QuesTeck Innovations LLC, Evanston Illinois 60201
  3. Department of Electrical Engineering and Computer Science, Northwestern University, Evanston Illinois 60208
Publication Date:
Grant/Contract Number:
SC0007456, DE-SC0014330
Type:
Publisher's Accepted Manuscript
Journal Name:
Journal of Computational Chemistry
Additional Journal Information:
Journal Volume: 39; Journal Issue: 4; Related Information: CHORUS Timestamp: 2017-12-12 01:42:43; Journal ID: ISSN 0192-8651
Publisher:
Wiley Blackwell (John Wiley & Sons)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
OSTI Identifier:
1395185