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  Gibbs–Helmholtz graph neural network: capturing the temperature dependency of activity coefficients at infinite dilution

Sanchez Medina, E. I., Linke, S., Stoll, M., & Sundmacher, K. (2023). Gibbs–Helmholtz graph neural network: capturing the temperature dependency of activity coefficients at infinite dilution. Digital Discovery, 2(3), 781-798. doi:10.1039/D2DD00142J.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000D-C323-F Version Permalink: https://hdl.handle.net/21.11116/0000-000E-7B60-C
Genre: Journal Article

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This is an open access publication which is licensed under a Creative Commons Attribution 3.0 Unported Licence
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https://github.com/edgarsmdn/GH-GNN (Research data)
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 Creators:
Sanchez Medina, Edgar Ivan1, 2, Author           
Linke, Steffen1, 2, Author           
Stoll, Martin3, Author
Sundmacher, Kai2, 4, Author           
Affiliations:
1International Max Planck Research School (IMPRS), Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738143              
2Otto-von-Guericke-Universität Magdeburg, External Organizations, ou_1738156              
3Chair of Scientific Computing, Department of Mathematics, Technische Universität Chemnitz, 09107 Chemnitz, Germany , ou_persistent22              
4Process 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: 2023
 Publication Status: Published online
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 Rev. Type: Peer
 Identifiers: DOI: 10.1039/D2DD00142J
Other: pubdata_escidoc:3528776
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Title: Digital Discovery
Source Genre: Journal
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Pages: - Volume / Issue: 2 (3) Sequence Number: - Start / End Page: 781 - 798 Identifier: ISSN: 2635-098X