A Diversified Machine Learning Strategy for Predicting and Understanding Molecular Melting Points

The ability to predict multi-molecule processes, using only knowledge of single molecule structure, stands as a grand challenge for molecular modeling. Methods capable of predicting melting points (MP) solely from chemical structure represent a canonical example, and are highly desirable in many crucial industrial applications. In this work, we explore a data-driven approach utilizing machine learning (ML) techniques to predict and understand the MP of molecules. Several experimental databases are aggregated from the literature to design a low-bias dataset that includes 3D structural and quantum-chemical properties. Using experimental and polymorph-induced uncertainties, we derive a tenable lower limit for MP prediction accuracy, and apply graph neural networks and Gaussian processes to predict MP competitive with these error bounds. To further understand how MP correlates with molecular structure, we employ several semi-supervised and unsupervised ML techniques. First, we use unsupervised clustering methods to identify classes of molecules, their common fragments, and expected errors for each data set. We then build molecular geometric spaces shaped by MP with a semi-supervised variational autoencoder and graph embedding spaces, and apply graph attribution methods to highlight atom-level contributions to MP within the datasets. Overall, this work serves as a case study of how to employ a diversified ML toolkit to predict and understand correlations between molecular structures and thermophysical properties of interest.