Gibbs-Helmholtz Graph Neural Network: capturing the temperature dependency of activity coefficients at infinite dilution

• Published in: arXiv.org 2022-12
• Main Authors: Sanchez Medina, Edgar Ivan, Linke, Steffen, Stoll, Martin, Sundmacher, Kai
• Format: Article
• Language: English
• Subjects: Activity coefficients; Chemical compounds; Chemical reactions; Dilution; Domains; Graph neural networks; Hydrogen bonding; Neural networks; Solvents; Temperature dependence
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2212.01199

The accurate prediction of physicochemical properties of chemical compounds in mixtures (such as the activity coefficient at infinite dilution \(\gamma_{ij}^\infty\)) is essential for developing novel and more sustainable chemical processes. In this work, we analyze the performance of previously-proposed GNN-based models for the prediction of \(\gamma_{ij}^\infty\), and compare them with several mechanistic models in a series of 9 isothermal studies. Moreover, we develop the Gibbs-Helmholtz Graph Neural Network (GH-GNN) model for predicting \(\ln \gamma_{ij}^\infty\) of molecular systems at different temperatures. Our method combines the simplicity of a Gibbs-Helmholtz-derived expression with a series of graph neural networks that incorporate explicit molecular and intermolecular descriptors for capturing dispersion and hydrogen bonding effects. We have trained this model using experimentally determined \(\ln \gamma_{ij}^\infty\) data of 40,219 binary-systems involving 1032 solutes and 866 solvents, overall showing superior performance compared to the popular UNIFAC-Dortmund model. We analyze the performance of GH-GNN for continuous and discrete inter/extrapolation and give indications for the model's applicability domain and expected accuracy. In general, GH-GNN is able to produce accurate predictions for extrapolated binary-systems if at least 25 systems with the same combination of solute-solvent chemical classes are contained in the training set and a similarity indicator above 0.35 is also present. This model and its applicability domain recommendations have been made open-source at https://github.com/edgarsmdn/GH-GNN.