f-electron structure database (FESD): A high-throughput data analysis and materials discovery tool for strongly correlated materials

We have developed a database with associated high-throughput computational tools for analyzing structural and electronic properties of f-electron materials such as the lanthanide and actinide compounds. Modeling of these systems is especially challenging due to the complex interplay between the effects of spin-orbit coupling, electron-electron interactions and the hybridization of the localized f-electrons with itinerant conduction electrons. This complexity drives richness of electronic properties, making these materials suitable for various technological applications. Here we adopt a data-driven approach to aid the materials discovery process. By deploying state-of-the-art algorithms and query tools, we train learning models using a large, simulated dataset based on existing f-electron crystals. The machine-learned models so obtained can then be used to search for new classes of stable materials with desired physical properties. We discuss the basic structure of our database and our approach toward cleaning and correcting the structure data files. Illustrative examples of the applications of our database include prediction of stable superstructures of double perovskites and identifying a number of interesting trends in strongly correlated features of f-electron based materials.