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Title: Deep neural network learning of complex binary sorption equilibria from molecular simulation data

Abstract

We employed deep neural networks (NNs) as an efficient and intelligent surrogate of molecular simulations for complex sorption equilibria using probabilistic modeling.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]
  1. Department of Chemistry and Chemical Theory Center, University of Minnesota, Minneapolis, USA
  2. Department of Chemistry and Chemical Theory Center, University of Minnesota, Minneapolis, USA, Department of Chemical Engineering and Materials Science
Publication Date:
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Bioenergy Technologies Office (EE-3B); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1503351
Grant/Contract Number:  
DEEE0006878; AC02-06CH11357; FG02-17ER16362
Resource Type:
Published Article
Journal Name:
Chemical Science
Additional Journal Information:
Journal Name: Chemical Science Journal Volume: 10 Journal Issue: 16; Journal ID: ISSN 2041-6520
Publisher:
Royal Society of Chemistry (RSC)
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Sun, Yangzesheng, DeJaco, Robert F., and Siepmann, J. Ilja. Deep neural network learning of complex binary sorption equilibria from molecular simulation data. United Kingdom: N. p., 2019. Web. doi:10.1039/C8SC05340E.
Sun, Yangzesheng, DeJaco, Robert F., & Siepmann, J. Ilja. Deep neural network learning of complex binary sorption equilibria from molecular simulation data. United Kingdom. doi:10.1039/C8SC05340E.
Sun, Yangzesheng, DeJaco, Robert F., and Siepmann, J. Ilja. Wed . "Deep neural network learning of complex binary sorption equilibria from molecular simulation data". United Kingdom. doi:10.1039/C8SC05340E.
@article{osti_1503351,
title = {Deep neural network learning of complex binary sorption equilibria from molecular simulation data},
author = {Sun, Yangzesheng and DeJaco, Robert F. and Siepmann, J. Ilja},
abstractNote = {We employed deep neural networks (NNs) as an efficient and intelligent surrogate of molecular simulations for complex sorption equilibria using probabilistic modeling.},
doi = {10.1039/C8SC05340E},
journal = {Chemical Science},
number = 16,
volume = 10,
place = {United Kingdom},
year = {2019},
month = {4}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1039/C8SC05340E

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Works referenced in this record:

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Assessment of Options for Selective 1-Butanol Recovery from Aqueous Solution
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