To prepare the x-vector, a shortened version of elemental properties, with focus on intermetallic elements, consisting of 85 properties were used. These properties were further applied with 8 mathematical equations (element with index 1 value, element with index 3 value, sum, difference, ratio, multiplication, largest value in the formula, smallest value in the formula), resulting in 680 feature columns. Later, 680 feature columns were filtered to avoid NaN (not a number) of values and infinity, which resulted in 614 features. In total, the matrix with size of (581+59+1) by 614 was set for processing and analysis. Support Vector Machine (SVM) Discriminant Analysis was used as the method within the PLS_Toolbox environment implemented in MATLAB. The model had optimized parameters with 376 support vectors. Statistics for prediction of each class are listed in Table . From model statistics, the most reliable structure class prediction is for Class 2 (Fe3C-type), most of the classed predicted reasonably well (considering the presence of polymorphs), while Class 4 (Mg3Cd-type) and Class 8 (TiAl3-type) produced the least reliable predictions. These statistics are reflected in the classification figures for Cu3Au-type (Figure ) and the rest of the structure types (Figure in Supplementary Information). As expected, the best performance was shown by the structure types that are significantly different from the rest of the structure, and feature either size/complexity of the structure or atypical homatomic interaction percent (Table ). For instance, Class 2 (Fe3C-type), which has the lowest classification error (5.4%) is a significantly different structure type, given the relative atomic size ratio of the constituent element, resulting in more homoatomic interaction percent than any other structure type, making it easier to identify by our ML model. In other words, the diverse atomic size scales that we added to the property table, including the predicted values, were valuable for the classification model. Similarly, the number of interactions in the nearest-neighbor environment (Table ) of Class 5 (PuNi3) suggests a more complex chemistry compared to the rest of the structure types, making it relatively easy to identify with ML as well, with the second lowest classification error (12.0%).