In ML, there are multiple terms that could be used interchangeably: descriptors, features, properties, variables. In the current study, we feel the need to differentiate some of the terms for the simplicity of understanding. When it comes to features generated from chemical composition, the properties of constituent elements are used in mathematical equations, typically weighted with the element's atomic percent or index to produce a feature that goes directly into the x-vector for the ML model (Figure ). In this approach, only the composition (element and index) is needed. Obviously, the more properties of the elements are in the property list, the more features could be generated. There are different opinions on the number of features that are necessary for a good ML model, since more features generate more noise as well, affecting the ML model in a negative way. However, if a feature selection algorithm is applied, the number of features is not a problem anymore, since from more features, a better combination of useful features could be extracted. The topic of feature selection is polarizing in the ML community, and while some prefer to avoid any manipulation (feature selection) on the data, the others prefer to minimize the noise by removing the variables that do not positively contribute to the model. A great starting point, in our opinion, might be to give researchers an opportunity to select what properties they want to use, by providing one of the most massive lists of elemental properties. A brief overview of featurizers is discussed here prior to the introduction of the Oliynyk property list. Featurizers in materials informatics, such as Atom2Vec, JARVIS, mat2vec, and Magpie are common and often are preferred by the community compared to in-lab built sets. Atom2Vec uses the known compounds in materials databases to propose properties of elements via unsupervised learning. Compound compositions are turned into an atom-environment matrix used for learning. Atom2Vec is capable of clustering elements in a high-dimensional vector space and classifying them into groups consistent with those found in the periodic table. Next, as a part of the Materials Genome Initiative by NIST, the Joint Automated Repository for Various Integrated Simulations (JARVIS) uses classical force-field inspired descriptors (CFID) which consists of 1,557 descriptors for each material. It is a very impressive number with a great consistency of the quality of descriptors, given the way they were generated. JARVIS-ML adopts more than 25 ML models for material-property predictions. Published literature can be also efficiently encoded as information-dense word embeddings (vector representations of words) with Word2vec, an ML algorithm that learns associated words and chemical formulas in 500,000 words acquired after processing 3.3 million abstracts. Mat2vec determines compounds that are similar in physical properties. Lastly, the Materials Agnostic Platform for Informatics and Exploration (Magpie) is a generalpurpose ML-based framework for predicting properties for a general set of materials data. The framework partitions data into similar groups and uses separate models for each group. With 145 features, Magpie was used to successfully propose appropriate compounds for solar cells with a band gap energy of 0.9-1.7 eV. Presented here, the Oliynyk property list, which we update and improve constantly, has been used in many experimentally-validated ML predictions and many other studies from various research groups. For instance, partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) techniques were applied to develop a crystal structure predictor for binary AB compounds. The ML model successfully predicted the CsCl-type structure of RhCd experimentally with 31 features from the list. In another study, a random forest algorithm with 22 descriptors and varying weights was used to predict 12 novel Heusler compounds. Cluster resolution feature selection (CR-FS) was incorporated along with SVM to predict the crystal structure of equiatomic ternary compositions with 113 features. 150 features were used to identiy a ternary rhenium tungsten carbide and a quaternary molybdenum tungsten borocarbide as a potential superhard material after screening 118,287 compounds. The selected compounds were synthesized and tested. SVM was used to predict Debye temperature and potential rare-earth substituted inorganic phosphor hosts. SVM algorithm with 23 features was used to determine site preferences of atoms within a solid crystal for intermetallics known as half-Heusler compounds. An ensemble ML method was used to find superhard materials by predicting load-independent Vickers hardness based on the chemical composition. The Oliynyk property list can be further combined with other databases, which was demonstrated in multiple studies. Graser et al. used the NIST-JANAF temperature-dependent thermochemical properties such as heat capacity, entropy, enthalpy, and heats of formations, and equilibrium constants, and elemental properties such as electronegativity differences, allowed valence states, stoichiometric sum, atomic radius, and nearest neighbor distance as features to generate regressions for determining temperaturedependent heat capacity for inorganic solids. Three regression models including linear regression, support vector regression, random forest were used. Zhuo et al. selected both experimentally measured bad gaps and 136 physical properties of composition's constituent elements to determine the band gaps of inorganic solids. Physical properties such as l quantum number, electronegativities, and valence electron numbers were used.