Researchers are using supercomputers to introduce and assess the impact of different configurations of defects on the performance of a superconductor. “When people think of targeted evolution, they might think of people who breed dogs or horses,” said Argonne materials scientist Andreas Glatz, the corresponding author of the study. “Ours is an example of materials by design, where the computer learns from prior generations the best possible arrangement of defects.”