Predicting Activity Coefficients at Infinite Dilution Using Hybrid Residual 
Graph Neural Networks

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

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Predicting Activity Coefficients at Infinite Dilution Using Hybrid Residual Graph Neural Networks

Sanchez Medina, E. I., Linke, S., Stoll, M., & Sundmacher, K. (2022). Predicting Activity Coefficients at Infinite Dilution Using Hybrid Residual Graph Neural Networks. Poster presented at 2022 AIChE Annual Meeting, Phoenix, USA.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-000C-150E-E Versions-Permalink: https://hdl.handle.net/21.11116/0000-000C-150F-D

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Urheber:
Sanchez Medina, Edgar Ivan^{1, 2}, Autor
Linke, Steffen^{1, 3}, Autor
Stoll, Martin⁴, Autor
Sundmacher, Kai^{2, 3}, Autor

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
3Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738151
4External Organizations, ou_persistent22