Automated Computation of Materials Properties
Materials informatics offers a promising pathway toward rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis techniques, underlying property trends can be identified, facilitating the formulation of new design rules. Such methods require large sets of consistently generated, programmatically accessible materials data. Computational materials design frameworks using standardized parameter sets are the ideal tools for producing such data. This work reviews the state-of-the-art in computational materials design, with a focus on these automated ab initio frameworks. Features such as structural prototyping and automated error correction that enable rapid generation of large data sets are discussed, and the way in which integrated workflows can simplify the calculation of complex properties, such as thermal conductivity and mechanical stability, is demonstrated. The organization of large data sets composed of ab initio calculations, and the tools that render them programmatically accessible for use in statistical learning applications are also described. Finally, recent advances in leveraging existing data to predict novel functional materials, such as entropy-stabilized ceramics, bulk metallic glasses, thermoelectrics, superalloys, and magnets, are surveyed.
