  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.
