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Scientists have developed a new machine learning approach that accurately predicted critical and difficult-to-compute properties of molten salts, materials with diverse nuclear energy applications. 

Analyzing massive datasets from nuclear physics experiments can take hours or days to process, but researchers are working to radically reduce that time to mere seconds using special software being developed at the Department of Energy’s Lawrence Berkeley and Oak Ridge national laboratories.  

A team from ORNL, joined by university students, recently traveled to the Ohio State University Research Reactor to conduct a novel experiment on nuclear thermal rocket fuel coatings — one that could help propel NASA’s astronauts to Mars faster and more efficiently. 

Jairus Hines, an electronics and unmanned systems technician at ORNL, works with airborne, waterborne and ground-based drones. As part of the lab’s Autonomous Systems group, he applies "low and slow" drone technology to radiation detection for national security missions.

During a recent visit to ORNL, several OASA (IE&E) representatives explored the ORNL’s leadership in advanced nuclear energy development to inform the design and construction of a microreactor to power mission-critical facilities at two Army bases. 

Scientists at ORNL have developed a method that can track chemical changes in molten salt in real time — helping to pave the way for the deployment of molten salt reactors for energy production.

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. 

Researchers at Georgia State University used the Summit supercomputer to study an elaborate molecular pathway called nucleotide excision repair. Decoding NER’s sophisticated sequence of events and the role of PInC in the pathway could provide key insights into developing novel treatments and preventing conditions that lead to premature aging and certain types of cancer.

During his first visit to Oak Ridge National Laboratory, 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.”

P&G is using simulations on the ORNL Summit supercomputer to study how surfactants in cleaners cause eye irritation. By modeling the corneal epithelium, P&G aims to develop safer, concentrated cleaning products that meet performance and safety standards while supporting sustainability goals.