Proposed here is the Structure Analyzer Featurizer (SAF) available at or . As of writing, SAF currently supports binary and ternary compounds, generating 94 numerical features and 134 features for ternary with the goal to support quaternary and higher compounds for future studies. The complete lists of features are available in the GitHub repository and in the Supplementary document in Tables S4-6 with Table providing comments that allow users to utilize extracted data not only for ML modeling but also for structure analysis. INT_* features are calculated from interatomic distance analysis, WYC_* features are based on Wyckoff symbol/multiplicity, ENV_* features are derived from atomic environment data, and CN_* features are also calculated from atomic environment data. Figure illustrates the process of procuring a single set of numerical features extracted from structural, compositional, and raw data as an input data source for ML models. Parts of the SAF code have been used to determine coordination geometry using various methods . Furthermore, although not implemented in this study, the features can be used for feature relationship analysis (e.g., SISSO) to reveal the relationships between measured structural features and properties. Open Database (COD), and Materials Studio. PCD provides detailed structural descriptions, including editor-entered crystal structure prototypes and fully standardized crystal structure data. Similarly, we have ensured that our code is compatible with the ICSD database, where most structures also have assigned structure types, which facilitates searches for specific structure classes. We recommend standardizing CIFs through trusted crystallographic software which writes CIFs in the correct format. Large CIF repositories do not guarantee consistency in CIF formatting, and even with large online databases, there could be cases where, for example, atomic label and atomic type are reversed, which might cause errors in file processing. Furthermore, CIFs, even from reputable databases, require some editing, due to typographic errors or missing entries, which prevents them from being parsed. Extracting data from databases might seem to be a straightforward process, but preparing the files for processing tends to require some adjustments. For instance, parsing errors might arise in cases where CIF info loops have blanks reported with some information missing. These could be as simple as the title of publication or the author's affiliation missing, but these problems can affect file parsing. In materials science, especially where experimental data is scarce, it is crucial to ensure that all reports are included and errors are automatically corrected. Another common CIF problem are inconsistent site labels, especially the numbering of labels or problematic labels in case of atomic mixing, where the same site could be labeled differently, causing confusion and inconsistent results during a high-throughput CIF processing. Therefore, to filter ill-formatted CIF files, we have also developed a standalone and user-interactive Python application called CIF Cleaner available at or . in Figure .