Table presents the classification performance of different approaches when predicting the phase behavior of group D-9F. Notably, our approach only has 2 misclassifications out of 57 samples, significantly outperforming the 22 and 38 misclassifications observed in the CD-RF and Curve-RF methods, respectively. Compared with using the entire SAXS curve as the input in the Curve-RF method, the six physically-formed features in PIMF-RF are informative in reducing the high dimensionality of the SAXS curve and they lead to much higher accuracy in predictions. Furthermore, the CD-RF approach does not rely on the use of SAXS data and it is shown to be accurate for predicting phases with monomers that appear in the training data set. The results indicate that the automated analysis of SAXS data in PIMF-RF effectively enhances the accuracy of predicting the phase of the novel monomers not in the training data set.