News

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. 

Researchers at ORNL have developed a tool that gives builders a quick way to measure, correct and certify level foundations. FLAT, or the Flat and Level Analysis Tool, examines a 360-degree laser scan of a construction site using ORNL-developed segmentation algorithms and machine learning to locate uneven areas on a concrete slab. 

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.  

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.

To help reduce the likelihood of losing future cultivated crops to drought and other seasonal hardships, researchers from ORNL, Budapest and Hungary are using neutrons, light microscopy and transmission electron microscopy to study the 'Never Never' plant, known for its ability to endure periods of little to no rain. 

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.

A team of scientists led by a professor from Duke University discovered a way to help make batteries safer, charge faster and last longer. They relied on neutrons at ORNL to understand at the atomic scale how lithium moves in lithium phosphorus sulfur chloride, a promising new type of solid-state battery material known as a superionic compound. 

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.

The ForWarn visualization tool was co-developed by ORNL with the U.S. Forest Service. The tool captures and analyzes satellite imagery to track impacts such as storms, wildfire and pests on forests across the nation.

Researchers have identified a molecule essential for the microbial conversion of inorganic mercury into the neurotoxin methylmercury, moving closer to blocking the dangerous pollutant before it forms.