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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Item Permalink: https://hdl.handle.net/21.11116/0000-000C-150E-E Version Permalink: https://hdl.handle.net/21.11116/0000-0010-22E9-1

Genre: Poster

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Creators:
Sanchez Medina, Edgar Ivan^{1, 2}, Author
Linke, Steffen^{1, 3}, Author
Stoll, Martin⁴, Author
Sundmacher, Kai^{2, 3}, 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
3Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738151
4External Organizations, ou_persistent22

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Language(s): eng - English

Dates: Created: 2022

Publication Status: Not specified

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Rev. Type: Peer

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Title: 2022 AIChE Annual Meeting

Place of Event: Phoenix, USA

Start-/End Date: 2022-11-13 - 2022-11-18

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