was estimated using an equation that depends on the rotational symmetry number and the molecular flexibility number of the target molecule. Using a database of 596 melting points containing only aliphatic non-hydrogen-bonding molecules, their method was found to have a root mean square error (RMSE) of 34.3 °C. Subsequent papers from this group have suggested that the largest source of error comes from the estimate of ∆𝑆 ! . The model was later expanded to include aromatic and aliphatic rings using a modified expression for the molecular flexibility number, yielding a comparable average absolute error (AAE) of 33.2 °C. Estimating ∆𝑆 ! is challenging, and limits the utility of methods that make use of the phase transition equation. developed a different GC method that accounts for the chemical environment around a chemical group via the use of second-order and third-order group contributions. Second and third-order group contributions are corrections to the original, first-order contribution. Special emphasis was given to improving the prediction of cyclic groups (both aliphatic and aromatic). When both second and third-order groups were used, the AAE was reduced to 20.22 °C (down from 24.9 °C when only first-order corrections were used). ML methods that incorporate molecular descriptors can serve as an alternative to GC-based methods. For example, Sivaraman et al. used a series of ML methods incorporating 2D and 3D