The enthalpy and Gibbs energy of sublimation are predicted using quantitative structure property relationship (QSPR) models. In this study, we compare several approaches previously reported in the literature for predicting the enthalpy of sublimation. These models, which were reproduced successfully, exhibit high correlation coefficients, in the range 0.82 to 0.97. There are significantly fewer examples of QSPR models currently described in the literature that predict the Gibbs energy of sublimation; here we describe several models that build upon the previous models for predicting the enthalpy of sublimation. The most robust and predictive model constructed using multiple linear regression, with the fewest number of descriptors for estimating this property, was obtained with an R2 of the training set of 0.71, an R2 of the test set of 0.62, and a standard deviation of 9.1 kJ mol-1. This model could be improved by training using a neural network, yielding an R2 of the training and test sets of 0.80 and 0.63, respectively, and a standard deviation of 8.9 kJ mol-1.