Autonomous data-driven design of inorganic materials with AFLOW

The expansion of programmatically-accessible materials data has cultivated opportunities for data-driven approaches. Highlyautomated frameworks like AFLOW not only manage the generation, storage, and dissemination of materials data, but also leverage the information for thermodynamic formability modeling, such as the prediction of phase diagrams and properties of disordered materials. In combination with standardized parameter sets, the wealth of data also presents a uniquely favorable learning environment. Machine learning algorithms have already been employed for structure and property prediction, descriptor development, design rule discovery, and the identification of candidate functional materials. These methods promise to revolutionize the path to synthesis and, ultimately, transform the practice of traditional materials discovery to one of rational and autonomous materials design.