Data Science, Simulation, and Experimental Methods for Exploration and Design of Superconducting Materials

The discovery of new superconductors has a significant impact on the scientific and engineering communities, unraveling interesting physical phenomena and providing unique applications in energy and devices. Superconductors with a high critical temperature are limited to a few families, such as cuprates, iron-based compounds, and hydrides under ultra-high pressure. In traditional studies, the exploration of new superconductors relies on theories, experiments, and simulations. However, recent advances in data science have made machine learning available in a variety of fields, including materials informatics. Utilizing superconductor databases and various regression methods, machine learning has proposed several new superconductors. The chemical descriptors are widely used, and the descriptor of the crystalline structure is being developed for more accurate prediction. In this review, the theoretical and experimental studies for the discovery of new superconductors are explained. The available database and data-driven studies are also shown. Furthermore, after reviewing the recent machine learning studies for the discovery of new superconductors and other materials, future aspects in this field are discussed.