Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids. (arXiv:2206.11776v1 [cs.LG])

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, …

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