Machine Learning Technique Could Improve Fusion Energy Outputs

Machine-learning techniques, best known for teaching self-driving cars to stop at red lights, may soon help researchers around the world improve their control over the most complicated reaction known to science: nuclear fusion. Fusion reactions are typically hydrogen atoms heated to form a gaseous cloud called a plasma that releases energy as the particles bang into each other and fuse. Getting these reactions under better control could create huge amounts of environmentally clean energy from nuclear reactors in fusion power plants of the future. "The connection between machine learning and fusion energy is not obvious," said Sandia researcher Aidan Thompson, principal investigator for a $2.2 million, three-year DOE Office of Science award to make that connection. "Simply put, we have pioneered machine-learning's use to improve simulations of the reactor's wall material as it interacts with the plasma. This has been beyond the scope of atomic-scale simulations of the past."