We next apply ML models to the quinone and hydroquinone datasets with Mordred-calculated features. Because the only information required to generate these datasets is the SMILES string and T m (V m is not a feature for these datasets), these datasets are much larger. We start with the ridge regression trained on the Mordred-generated features (Approach II.a). We find that the Mordred-featurized ridge regression model for the quinone dataset both qualitatively and quantitatively has higher predictive power than for the hydroquinone dataset. The quinone dataset AAEs are consistently less than 30 °C for both the training and test sets, while those for the hydroquinone dataset are usually 39-40 °C. Qualitatively, we also see that the quinone dataset follows the ideal prediction (dashed) trend line. In contrast, the hydroquinone models appears flattened, such that the melting points of the lower T m molecules are overpredicted and those of the higher T m molecules are underpredicted. We hypothesize that melting point has some underlying attribute that is not as well-captured by the Mordred-generated features for the hydroquinones as it is for the quinones. As the molecular features are generated based just on the SMILES string of the molecule, it is unlikely that complexities such as intermolecular interactions (which we would expect to be more important for hydroquinones than quinones due to hydrogen bonding) are welldescribed by these calculated features.