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  Transformer-convolutional neural network for surface charge density profile prediction: Enabling high-throughput solvent screening with COSMO-SAC

Chen, G., Song, Z., & Qi, Z. (2021). Transformer-convolutional neural network for surface charge density profile prediction: Enabling high-throughput solvent screening with COSMO-SAC. Chemical Engineering Science, 246: 117002. doi:10.1016/j.ces.2021.117002.

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10.1016_j.ces.2021.117002.pdf (Publisher version), 2MB
 
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 Creators:
Chen, Guzhong1, Author
Song, Zhen1, 2, 3, Author              
Qi, Zhiwen1, Author
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1State Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China, ou_persistent22              
2Process Systems Engineering, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, Magdeburg, D-39106, Germany, ou_persistent22              
3Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738151              

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Language(s): eng - English
 Dates: 2021
 Publication Status: Published in print
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 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.ces.2021.117002
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Title: Chemical Engineering Science
Source Genre: Journal
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Pages: - Volume / Issue: 246 Sequence Number: 117002 Start / End Page: - Identifier: ISSN: 00092509