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

Abstract

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.

Code available at: https://github.com/edgarsmdn/GH-GNN

References in corpus (4)

• On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
• Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
• Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
• A smile is all you need: Predicting limiting activity coefficients from SMILES with natural language processing

Cited by in corpus (6)

• Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction
• Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction
• Thermodynamics-Consistent Graph Neural Networks
• Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
• GRAPPA -- A Hybrid Graph Neural Network for Predicting Pure Component Vapor Pressures
• GraphXForm: Graph transformer for computer-aided molecular design