  Even though superconductivity has been studied intensively for more than a
century, the vast majority of superconductivity research today is carried out
in nearly the same manner as decades ago. That is, each study tends to focus on
only a single material or small subset of materials, and discoveries are made
more or less serendipitously. Recent increases in computing power, novel
machine learning algorithms, and improved experimental capabilities offer new
opportunities to revolutionize superconductor discovery. These will enable the
rapid prediction of structures and properties of novel materials in an
automated, high-throughput fashion and the efficient experimental testing of
these predictions. Here, we review efforts to use machine learning to attain
this goal.
