An important and long-standing goal in predictive chemistry is the development of an accurate melting point model that is generally applicable to a wide range of molecules. Many decades of work have been invested in this endeavor, but thus far, none are sufficiently accurate to make useful and confident predictions for compounds without a measured melting point. Indeed, an improved understanding of melting point would be useful across large areas of science, including, but not limited to, materials for energy applications, ) drug discovery , organic hydrogen getters , ionic liquids , and separations chemistry as well as the prediction of properties such as viscosity . Accurate predictions of melting points are difficult because large differences in melting points can arise from small changes to molecular structure. Existing melting point models fall broadly into two categories: those that leverage group contribution (GC) theory and those that use machine learning (ML) techniques. the solid-liquid phase transition equation, 𝑇 ! = ∆𝐻 ! ∆𝑆 ! & , to develop a melting point model . The enthalpy of melting (∆𝐻 ! ) was estimated using GC methods, and the entropy of melting (∆𝑆 ! )