Materials informatics uses data-driven approaches for the study and discovery of materials. Features or descriptors are the crucial components in generating reliable and accurate machine-learning models. While general data can be acquired through public and commercial sources, features must be tailored for a specific application. Common featurizers are suitable for generic chemical problems, but may not be ideal for solid state materials. Here, we have assembled the Oliynyk property list for feature generation which works well on limited datasets (50 to 1,000 training data points) in solid state materials domain. We applied Gaussian process regression to extrapolate and predict missing values in the element property list. Complete data in the feature list allows researchers to use any methods that require a complete x-block without any data gaps, such as SVM. To validate our updated property list and generated features based on this list, a classical crystallographic problem of classifying structure type was solved. Similarly to the radius ratio rule (Linus Pauling, 1929) and structure maps (Villars, 1983; Pettifor 1984), we demonstrate how 1:3 stoichiometry structure types could be classified with SVM method and the x-block based on the proposed list of properties. We validate the ML model experimentally by synthesizing a novel intermetallic UCd3.

The ternary rare-earth-metal nickel indides RE 23 Ni 7 In 4 (RE = Gd, Tb, Dy) were prepared by arc-melting mixtures of the elements followed by annealing at 870 K. They adopt the Yb 23 Cu 7 Mg 4 -type structure (space group P6 3 /mmc, Pearson symbol hP68, Z = 2), as determined by laboratory and synchrotron powder diffraction methods for RE = Gd (a = 9.6435(10) Å, c = 22.118(3) Å) and Tb (a = 9.5695(8) Å, c = 21.983(3) Å), and single-crystal X-ray diffraction methods for RE = Dy (a = 9.533(5) Å, c = 21.890(13) Å). The centrosymmetric Yb 23 Cu 7 Mg 4 -type structure is closely related to the noncentrosymmetric Pr 23 Ir 7 Mg 4 -type structure. Triangular In 3 clusters within RE 23 Ni 7 In 4 represent a rare type of cluster found among metal-rich indides; the reasons for their formation were investigated by density functional theory methods.