Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids

Abstract

Ionic liquids (ILs) are important solvents for sustainable processes and predicting activity coefficients (ACs) of solutes in ILs is needed. Recently, matrix completion methods (MCMs), transformers, and graph neural networks (GNNs) have shown high accuracy in predicting ACs of binary mixtures, superior to well-established models, e.g., COSMO-RS and UNIFAC. GNNs are particularly promising here as they learn a molecular graph-to-property relationship without pretraining, typically required for transformers, and are, unlike MCMs, applicable to molecules not included in training. For ILs, however, GNN applications are currently missing. Herein, we present a GNN to predict temperature-dependent infinite dilution ACs of solutes in ILs. We train the GNN on a database including more than 40,000 AC values and compare it to a state-of-the-art MCM. The GNN and MCM achieve similar high prediction performance, with the GNN additionally enabling high-quality predictions for ACs of solutions that contain ILs and solutes not considered during training.

16 pages, 4 figures, 5 tables

References in corpus (5)

• Directional Message Passing for Molecular Graphs
• Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
• A smile is all you need: Predicting limiting activity coefficients from SMILES with natural language processing
• Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
• Graph neural networks for the prediction of molecular structure-property relationships

Cited by in corpus (10)

• SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients
• 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 solvation free energies for neutral molecules in any solvent with openCOSMO-RS
• Gibbs-Helmholtz Graph Neural Network: capturing the temperature dependency of activity coefficients at infinite dilution
• Graph Neural Networks for Surfactant Multi-Property Prediction
• 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