For instance, it helps to develop new size or electronegativity scales for specific subclass of compounds or analyze polyhedra through the lens of electron configurations and orbital hybridization theory. Structural features play an important role in the overall prediction of property schema in various situations. For instance, in a study analyzing ligand affinities, the authors ranked the features with three different methods, namely RF, Permutation Importance, and AdaBoost which consistently placed PEOE_VSA2 and NumHAcceptors as the two highest-ranked features. These features are two-dimensional topological and topochemical properties that have versatile uses. However, the authors specifically needed them to provide valuable information about the molecular surface and its potential interactions with binding species. NumHAcceptors is self-explanatory, while PEOE_VSA calculates the atomic contributions to the van der Waals surface area using partial total charge and molar refractivity. In another paper predicting band gap for materials, the authors analyzed the respective features by using two different ranking criteria, one based on Pearson Correlation between each of the fifteen features and the target variable and the other based on the weights obtained from Lasso regularization (weights of the Lasso coefficient). They were able to reduce the original set of fifteen features to seven with no loss of information. Another work had a similar schema using both a low number of descriptors (nine in total, a combination of elemental and structurally based) and found only one of those structural descriptors (octahedral factor) in the top five descriptor ranking. They ranked these descriptors using recursive feature elimination, which selects features by recursively removing those which exhibit the smallest weight assigned by an extra trees classifier. Structural features were also used for bulk and shear modulus prediction (proxy properties for hardness), where they were among the most important features after iterative feature selection. As we can see, structural features are used in building explainable models and could be easily identified in datasets with a small number of features. This allows detailed feature correlation analysis and straight-forward construction of the decision trees, which are regarded as the most visual representation of model explainability.