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Illustration of a quantum experiment: atoms in a lattice (inset) with entanglement effects radiating from a central particle on a textured surface.

Working at nanoscale dimensions, billionths of a meter in size, a team of scientists led by ORNL revealed a new way to measure high-speed fluctuations in magnetic materials. Knowledge obtained by these new measurements could be used to advance technologies ranging from traditional computing to the emerging field of quantum computing. 

ORNL researcher Melissa Cregger is standing outside smiling for a photo. Woman is wearing blue and white polka dot shirt with a purple cardigan.

Melissa Cregger of the Department of Energy’s Oak Ridge National Laboratory has received the Presidential Early Career Award for Science and Engineers, or PECASE, the highest honor bestowed by the U.S. government on outstanding early-career scientists and engineers. 

Procter & Gamble scientists used ORNL’s Summit supercomputer to create a digital model of the corneal epithelium, the primary outer layer of cells covering the human eye, and test that model against a series of cleaning compounds in search of a gentler, more environmentally sustainable formula.

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.

Two pictures of a rounded triangle shape are shown in mirror image. The left is white with red and purple spots in the middle while the one on the right is purple with a yellow and blue ring in the middle

Scientists designing the world’s first controlled nuclear fusion power plant, ITER, needed to solve the problem of runaway electrons, negatively charged particles in the soup of matter in the plasma within the tokamak, the magnetic bottle intended to contain the massive energy produced. Simulations performed on Summit, the 200-petaflop supercomputer at ORNL, could offer the first step toward a solution.

ORNL researcher Phong Le poses for a photo outside on a walkway bridge over the pond. The photo is framed with brown and green plants

Phong Le is a computational hydrologist at ORNL who is putting his skills in hydrology, numerical modeling, machine learning and high-performance computing to work quantifying water-related risks for humans and the environment. 

Image is blue and green with the background being a building on the left, merging into the photo on the right which are pictures of doppler radar graphics

Researchers at the Department of Energy’s Oak Ridge National Laboratory 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. 

Man is flying drone in hurricane aftermath, holding the controller

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. 

Three speakers are presenting at the front of the room with a presentation on the pulled down screen in the background
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
Researcher in a blue coat and glasses, purple gloves and white baseball gat pulls out materials from a metal canister

ORNL researchers created and tested two methods for transforming coal into the scarce mineral graphite, which is used in batteries for electric vehicles. 

Pictured is a map that is color-coded into purple, black, orange, pink and yellow to depict building density and color based on morphology to predict height

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.