Summit supercomputer draws molecular blueprint for repairing damaged DNA
In collaboration with the U.S. Department of Homeland Security’s Science and Technology Directorate, researchers at ORNL are evaluating technology to detect compounds emitted by pathogens and pests in agricultural products at the nation’s border.
Professionals from government and industry gathered at ORNL for the Nondestructive Assay Holdup Measurements Training Course for Nuclear Criticality Safety, a hands-on training in nondestructive assay, a technique for detecting and quantifying holdup without disturbing operations.
During his first visit to 91°µÍø, Energy Secretary Chris Wright compared the urgency of the Lab’s World War II beginnings to today’s global race to lead in artificial intelligence, calling for a “Manhattan Project 2.â€
Using the Frontier supercomputer at ORNL, researchers have developed a new technique that predicts nuclear properties in record detail. The study revealed how the structure of a nucleus relates to the force that holds it together. This understanding could advance efforts in quantum physics and across a variety of sectors, from to energy production to national security.
Scientists at ORNL are using advanced germanium detectors to explore fundamental questions in nuclear physics, such as the nature of neutrinos and the matter-antimatter imbalance. The ongoing LEGEND project, an international collaboration, aims to discover neutrinoless double beta decay, which could significantly advance the understanding of the universe.
Researchers at the Department of Energy’s 91°µÍø are using non-weather data from the nationwide weather radar network to understand how to track non-meteorological events moving through the air for better emergency response.
During Hurricanes Helene and Milton, ORNL deployed drone teams and the Mapster platform to gather and share geospatial data, aiding recovery and damage assessments. ORNL's EAGLE-I platform tracked utility outages, helping prioritize recovery efforts. Drone data will train machine learning models for faster damage detection in future disasters.
A team of researchers at ORNL are using virtual reality to understand normal and abnormal human behavior in a given location – specifically, a nuclear reactor. As people move around their lives, they tend to do similar activities in the same
Researchers are using machine learning to provide a more complete picture of building geometries that include building height to within three meters of accuracy. This model not only provides building height for any building in the world, but it will also feed into LandScan and other large government datasets for planning and response. 
In early November, ORNL hosted the International Atomic Energy Agency (IAEA) Interregional Workshop on Safety, Security and Safeguards by Design in Small Modular Reactors, which welcomed 76 attendees representing 15 countries, three U.S. national labs, domestic and international industry partners, as well as IAEA officers.