  Data scarcity, bias, and experimental noise are all frequently encountered
problems in the application of deep learning to chemical and material science
disciplines. Transfer learning has proven effective in compensating for the
lack in data. The use of quantum calculations in machine learning enables the
generation of a diverse dataset and ensures that learning is less affected by
noise inherent to experimental databases. In this work, we propose a transfer
learning approach for the prediction of solvation free energies that combines
fundamentals from quantum calculations with the higher accuracy of experimental
measurements. The employed model architecture is based on the directed-message
passing neural network for the molecular embedding of solvent and solute
molecules. A significant advantage of models pre-trained on quantum
calculations is demonstrated for small experimental datasets and for
out-of-sample predictions. The improved out-of-sample performance is shown for
new solvents, for new solute elements, and for the extension to higher molar
mass solutes. The overall performance of the pre-trained models is limited by
the noise in the experimental test data, known as the aleatoric uncertainty. On
a random test split, a mean absolute error of 0.21 kcal/mol is achieved. This
is a significant improvement compared to the mean absolute error of the quantum
calculations (0.40 kcal/mol). The error can be further reduced to 0.09 kcal/mol
if the model performance is assessed on a more accurate subset of the
experimental data.
