On the basis of test set AAE, the best model for the quinone dataset was the Mordredfeaturized ridge regression (Figure Thermodynamic data (V m ) is challenging to find for some compounds, so models with these features are harder to use. However, these models do offer physical insights, and in the hydroquinone case, outperform the model with the Mordred-calculated features. We hypothesized that V m could serve as a proxy for the crystal lattice energy, which had been found by others to be an important contributor to melting point. The V m feature was one of the most important predictors of melting point (in the hydroquinone ridge regression model -Table and in both random forest models -Table ). For molecules with stronger anticipated intermolecular interactions (hydrogen bonding in hydroquinones) the model with thermodynamic features provides an advantage over models based on features calculated only with the 2D structure of individual molecules. In order to fully realize the advantage of the thermodynamic featurization applied to a large number of molecules, V m can be determined computationally for a new molecule.