  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.

Machine Learning;Chemical Physics