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  Gibbs–Helmholtz Graph Neural Network for the Prediction of Activity Coefficients of Polymer Solutions at Infinite Dilution

Sanchez Medina, E. I., Kunchapu, S., & Sundmacher, K. (2023). Gibbs–Helmholtz Graph Neural Network for the Prediction of Activity Coefficients of Polymer Solutions at Infinite Dilution. The Journal of Physical Chemistry A, 127, 9863-9873. doi:10.1021/acs.jpca.3c05892.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000E-1AE2-6 Version Permalink: https://hdl.handle.net/21.11116/0000-000E-1AE3-5
Genre: Journal Article

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Copyright © 2023 The Authors. Published by American Chemical Society. This publication is licensed under CC-BY 4.0.
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 Creators:
Sanchez Medina, Edgar Ivan1, 2, Author           
Kunchapu, Sreekanth2, Author
Sundmacher, Kai2, 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              

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Language(s): eng - English
 Dates: 2023
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1021/acs.jpca.3c05892
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Title: The Journal of Physical Chemistry A
Source Genre: Journal
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Pages: - Volume / Issue: 127 Sequence Number: - Start / End Page: 9863 - 9873 Identifier: ISSN: 1089-5639
ISSN: 1520-5215