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

Chen, Guzhong; Song, Zhen; Qi, Zhiwen

doi:10.1016/j.ces.2021.117002

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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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Item Permalink: http://hdl.handle.net/21.11116/0000-0009-552B-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0009-552C-7

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Creators:
Chen, Guzhong¹, Author
Song, Zhen^{1, 2, 3}, Author
Qi, Zhiwen¹, Author

Affiliations:
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: Published in Print: 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