Computers Use Machine Learning to Detect Radiation Damage Better Than Humans Do

Developing safe nuclear reactor materials depends on a critical, though tedious and time-consuming, task: sifting through electron microscopy images of materials exposed to radiation to identify radioactive damage. This monotonous task has traditionally fallen to image-processing algorithms programmed to identify patterns in images that look like Jackson Pollock paintings. Researchers at the University of Wisconsin-Madison and Oak Ridge National Laboratory may have found a faster and more accurate alternative: letting computers learn how to identify the damage by themselves. "Human detection and identification is error-prone, inconsistent and inefficient," said Dane Morgan, materials science and engineering professor. "Newer imaging technologies are outstripping human capabilities to analyze the data we can produce."