NUCLEAR SCIENCEArtificial Intelligence Reframes Nuclear Material Studies
The future of nuclear energy, which can produce electricity without harmful emissions, depends on discovery of new materials. A scientist at Argonne is using computer vision to separate the best candidates from a crowded field.
If a picture can tell a thousand words, imagine the frame-by-frame story that can be gleaned from a single video. Five minutes of video containing 200 frames per second can result in 60,000 images — a visual “Moby Dick.” Sound tedious to digest and catalog? It is, which explains why scientists don’t usually analyze their experiments’ videos in such detail.
Wei-Ying Chen, a principal materials scientist in the nuclear materials group at the Department of Energy’s (DOE) Argonne National Laboratory, is experimenting with advances in artificial intelligence (AI) to change that. The deep learning-based multi-object tracking (MOT) algorithm he uses to extract data from videos, as detailed in a recently published study, aims to help the U.S. improve advanced nuclear reactor designs. In turn, modernized nuclear power would better produce safe, reliable electricity without releasing harmful greenhouse gases.
Currently, nuclear energy produces more electricity on less land than any other clean energy source. Many commercial nuclear reactors, which supply nearly 20% of total U.S. electricity, use older materials and technology. Scientists and engineers believe newer materials and advanced designs could substantially increase the percentage of clean electricity generated by nuclear power plants.
“We want to build advanced reactors that can run at higher temperatures, so we need to discover materials that are resistant to higher temperature and higher irradiation dose,” said Chen. “With computer vision tools, we are on track to get all the data we need from all of the video frames.”
Chen assists users and conducts experiments at Argonne’s Intermediate Voltage Electron Microscope (IVEM) facility, a national user facility and a partner facility of DOE’s Nuclear Science User Facilities (NSUF). The IVEM – part transmission electron microscope, part ion beam accelerator — is one of about a dozen instruments in the world that let researchers look at material changes caused by ion irradiation as the changes happen (in situ). This means scientists like Chen can study the effects of different energies on materials proposed for use in future nuclear reactors.