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

Sanchez Medina, Edgar Ivan; Linke, Steffen; Stoll, Martin; Sundmacher, Kai

doi:10.1039/D2DD00142J

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

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Sanchez Medina, Edgar Ivan
International Max Planck Research School (IMPRS), Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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Linke, Steffen
International Max Planck Research School (IMPRS), Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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Sundmacher, Kai
Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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https://github.com/edgarsmdn/GH-GNN
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Citation

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.

Cite as: https://hdl.handle.net/21.11116/0000-000D-C323-F

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