Machine learning descriptors in materials chemistry used in multiple experimentally validated studies: Oliynyk elemental property dataset

Materials informatics employs data-driven approaches for analysis and discovery of materials. Features also referred to as descriptors are essential in generating reliable and accurate machine-learning models. While general data can be obtained through public and commercial sources, features must be tailored to specific applications. Common featurizers suitable for generic chemical problems may not be effective in features-property mapping in solid-state materials with ML models. Here, we have assembled the Oliynyk property list for compositional feature generation, which performs well on limited datasets (50 to 1,000 training data points) in the solid-state materials domain. The dataset contains 98 elemental features for atomic numbers from 1 to 92, including thermodynamic properties, electronic structure data, size, electronegativity, and bulk properties such as melting point, density, and conductivity. The dataset has been utilized peer-reviewed publications in predicting material hardness, classification, discovery of novel Heusler compounds, band gap prediction, and determining the site preference of atoms using machine learning models including support vector machines, random forests for classification, and support vector regression for regression problems. We have compiled the dataset by parsing data from publicly available databases and literature and further supplementing it by interpolating values with Gaussian process regression.