Clustering with CAF (Figure ) also was not better than others composition-based featurizers, which is not surprising given that it is based on the OLED set of properties for feature generation. The location of some datapoints with extreme values of LV1 and LV2 might indicate decreased confidence in their prediction as they are approaching the limits of the compositional space. JARVIS had precious metal silicides at the edge of the model confidence, which is typical for underrepresented cases such as OsSi (FeSi-type), IrSi (FeAstype), and RhSi (in both FeAs-and CsCl-types). MAGPIE had some issues with classifying rare cases when the compounds are formed with two p-block elements, such as GaSb (ZnS-type), InSb (ZnS-type), and SnSb (ZnS-and NaCl-type). This is not surprising as most of the dataset consisted of transition metal containing phases, and compounds with only main block elements are rare. Similar issues, and actually the same compounds were problematic on the PLS-DA plot from the model based on OLED. One of the limitations of compositionbased featurizers is their inability to handle polymorph cases, where one stoichiometry can form multiple structures, like it was in the cases described above (SnSb and RhSi).