  Superconducting materials find applications in a rapidly growing number of
technological areas, and searching for novel superconductors continues to be a
major scientific task. However, the steady increase in the complexity of
candidate materials presents a big challenge to the researchers in the field.
In particular, conventional experimental methods are not well suited to
efficiently search for candidates in compositional space exponentially growing
with the number of elements; neither do they permit 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 superconducting materials research are urgently needed.
High-throughput methods for rapid screening and optimization of materials have
demonstrated their utility for accelerating research in bioinformatics and
pharmaceutical industry, yet remain rare in quantum materials research. In this
paper, we will briefly review the history of high-throughput research paradigm
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 of superconductivity research in the near future.
