Quinones and hydroquinones are small organic molecules
with numerous
applications: battery electrolytes, pharmaceuticals, and sensors,
to name a few. An understanding of their fundamental properties, such
as melting points, is essential to incorporating these compounds into
relevant technologies. In this study, two different approaches were
investigated to predict the melting points of quinone- and hydroquinone-based
molecules. In one approach, molecular features were calculated with
the Mordred molecular descriptor calculator and used to train a ridge
regression and a random forest machine learning (ML) model. In the
other, a simpler featurization that captures key enthalpic and entropic
descriptors was applied in three model variants: a thermodynamic model,
a ridge regression model, and a random forest model. On the basis
of the average absolute error (AAE), the Mordred-calculated features
in the ridge regression model outperformed the thermodynamic features
across all models for the quinone data set (AAE: 29.5 °C), but
the thermodynamic features in the thermodynamic model resulted in
lower prediction errors for the hydroquinone data set (AAE: 35.7 °C).
These results emphasize the importance of including intermolecular
interaction descriptors, especially for classes of molecules in which
these interactions are expected to be strong (e.g., hydrogen bonding
in hydroquinones). As a byproduct of this study, we also consolidate
(from previously published data) four new data sets with quinone and
hydroquinone melting points and other key features.