Quinone-and hydroquinone-based molecules have gathered attention as candidate molecules for a variety of purposes -redox flow battery electrolytes, 1-9 electrochemical CO 2 capture, fluorescence and colorimetric sensors, and pharmaceutical products, among other applications. Recently, eutectic mixtures of small organic molecules have also gained attention as candidate materials for flow battery electrolytes. In order to identify promising materials for eutectic electrolytes, determining the melting temperatures of the quinone and hydroquinone molecules is essential. With an effective approximation of the regular solution model, the melting points of the single-component quinones and hydroquinones can be used to predict the melting point of a eutectic mixture. However, melting data is not readily available for all the quinones and hydroquinones of interest, so having a model that predicts the melting points of quinone and hydroquinone molecules is highly desirable. Various approaches are employed to predict organic compound melting points. Many models employ group contribution methods (GCMs). These are additive methods that sum the contributions from all of the functional groups within a molecule. However, these methods do not account for intermolecular interactions between functional groups on different molecules. Treating molecules as isolated species neglects effects such as Van der Waals (VdW) and hydrogen bonding, which are key contributors to macroscopic properties such as melting point. Karthikeyan et al. recognized this in their machine-learning (ML) based approach to predicting melting points of organic chemicals. They attributed most of the error in their model to the use of single-molecule based descriptors, which do not account for intermolecular interactions and crystal packing. Salahinejad et al. found a "reasonably linear" relationship between the melting point and sublimation enthalpy for small organic molecules, further emphasizing the need for macroscopic descriptors in predicting melting point. Crystal lattice energy, for example, is an important feature for predicting melting point, particularly where intermolecular interactions are expected to be important. Average absolute error (AAE), the average magnitude of the difference between predicted and experimental melting points across all compounds, is typically within the range of 30 -40 °C and often between 35 -40 °C for the test set.