Oak Ridge National Laboratory researchers have developed a generative machine-learning workflow that learns the statistical fingerprints of damaged tungsten and produces synthetic microstructures for fusion-material testing. The April 15 study could reduce the time and cost of assessing plasma-facing components for future reactors.