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Title: Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning

Authors:
; ; ; ; ORCiD logo;  [1]
  1. Department of Computer Science, University of North Carolina Charlotte, Charlotte, North Carolina 28223, United States
Publication Date:
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1484052
Grant/Contract Number:  
SC0007167
Resource Type:
Journal Article: Published Article
Journal Name:
Journal of Physical Chemistry. C
Additional Journal Information:
Journal Name: Journal of Physical Chemistry. C; Journal ID: ISSN 1932-7447
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English

Citation Formats

Chowdhury, Asif J., Yang, Wenqiang, Walker, Eric, Mamun, Osman, Heyden, Andreas, and Terejanu, Gabriel A.. Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning. United States: N. p., 2018. Web. doi:10.1021/acs.jpcc.8b09284.
Chowdhury, Asif J., Yang, Wenqiang, Walker, Eric, Mamun, Osman, Heyden, Andreas, & Terejanu, Gabriel A.. Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning. United States. doi:10.1021/acs.jpcc.8b09284.
Chowdhury, Asif J., Yang, Wenqiang, Walker, Eric, Mamun, Osman, Heyden, Andreas, and Terejanu, Gabriel A.. Fri . "Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning". United States. doi:10.1021/acs.jpcc.8b09284.
@article{osti_1484052,
title = {Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning},
author = {Chowdhury, Asif J. and Yang, Wenqiang and Walker, Eric and Mamun, Osman and Heyden, Andreas and Terejanu, Gabriel A.},
abstractNote = {},
doi = {10.1021/acs.jpcc.8b09284},
journal = {Journal of Physical Chemistry. C},
number = ,
volume = ,
place = {United States},
year = {Fri Nov 30 00:00:00 EST 2018},
month = {Fri Nov 30 00:00:00 EST 2018}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1021/acs.jpcc.8b09284

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