To compare the featurizers and test how the combination of compositional and structural features can influence the output, we prepared features using CBFV-embedded JARVIS, MAGPIE, mat2vec, and Oliynyk (OLED) featurizers and two sets of features prepared using the featurizers described in this work: Composition Analyzer Featurizer (CAF) and Structure Analyzer Featurizer (SAF). The number of features that were generated is available in Table . In the cases where feature calculation resulted in infinity (Inf) or not a number value (NaN), these columns had to be ignored for the purpose of ML model training. These cases occur with generic featurizers quite commonly, given that division by zero occurs when a set of standard mathematical operations looped through the list of element properties. One could replace these Inf and NaN with the best guess value; however, it is advised not to do that for preserving data in its original state. With our newest developments, CAF and SAF, we made sure to avoid calculations that result in problems, for example, by never dividing any property by the number of electrons at certain shells, which might result in the division by zero. For each feature case study, we employed three very common ML methods: PLS-DA, SVM, and XGBoost. The support vector machine (SVM) model produces similar results in terms of model statistics and time to train compared to XGBoost. In the current dataset, neither of the featurizers had issues with providing features sufficient to train and cross validate SVM models with the best model statistics (Table ). Along with our feature sets, mat2vec also had marginally higher precision and recall compared to other datasets. As expected, we observed the ideal predictions with more expensive (SVM) models, which already showed effectiveness in crystallographic problem solving previously. Our goal in this study is not attaining the best model statistics. The primary purpose of the study was to test how different sets of features perform with simpler methods (Partial least square discriminant analysis, PLS-DA) and to assess the visual clustering achieved through dimensionality reduction (latent values, LV, and individual feature contribution), similar to structure map approaches (Figure ). The performance of the models on each dataset is available here: CAF-performance. The estimated cost value in Table is based on the time it takes to run all models (PLS-DA, SVM, and XGBoost) on a single core.