Crystal structure map for materials classification and modeling

For classifying and modeling properties of crystalline materials in terms of structure, a three-step workflow with (1) generation of structure feature vectors, (2) evaluation of distances among the feature vectors as a measure of similarity in structure, and (3) mapping of each structure in a low-dimensional space with principal components using dimension reduction is proposed. The obtained distance and resulting principal components are useful for classifying similar crystal structures for a given set of materials systems and for constructing descriptors in machine-learning analyses of properties. The eigenvector of the principal components indicates which part of the original structure feature vector is contained as important information. Examples are demonstrated for classification and property modeling of Al[Formula: see text]O[Formula: see text] polymorphs including amorphous structures and of the alloy configurations of Si-doped LaFe[Formula: see text] compounds.