Melting point prediction is an immensely challenging problem that lacks a rapid, reliable solution. With this work, we demonstrated that a physical understanding of the molecules of interest and their bonding environments, along with a thermodynamics-based understanding of T m , can aid in categorizing molecules to enable melting point prediction from very few features (with an accuracy comparable to that of the ∼ 1,000 automatically calculated Mordred 2D descriptors). The errors from our best-performing models were on par (hydroquinones)