Recent advances in high-throughput superconductivity research

Applications are being found for superconducting materials in a rapidly growing number of technological areas, and the search for novel superconductors remains a major scientific task. However, the steady increase in the complexity of candidate materials presents a big challenge to researchers. In particular, conventional experimental methods are not well suited to an efficient search for candidates in a compositional space growing exponentially with the number of elements; neither do they permit a quick extraction of reliable multidimensional phase diagrams delineating the physical parameters that control superconductivity. New research paradigms that can boost the speed and the efficiency of research into superconducting materials are urgently needed. High-throughput methods for the rapid screening and optimization of materials have aided the acceleration of research in bioinformatics and the pharmaceutical industry, yet remain rare in quantum materials research. In this paper we briefly review the history of high-throughput research and then focus on some recent applications of this paradigm in superconductivity research. We consider the role these methods can play in all stages of materials development, including high-throughput computation, synthesis, characterization, and the emerging field of machine learning for materials. The high-throughput paradigm will undoubtedly become an indispensable tool in superconductivity research in the near future.