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ORNL's Communications team works with news media seeking information about the laboratory. Media may use the resources listed below or send questions to news@ornl.gov.

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Illustration of the GRETA detector, a spherical array of metal cylinders. The detector is divided into two halves to show the inside of the machine. Both halves are attached to metal harnesses, displayed against a black and green cyber-themed background.

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.  

Artist's rendering depicts a cantilever's sharp tip in an atomic force microscope scanning a material's surface to measure domain wall movement

As demand for energy-intensive computing grows, researchers at ORNL have developed a new technique that lets scientists see how interfaces move in promising materials for computing and other applications. The method, now available to users at the Center for Nanophase Materials Sciences at ORNL, could help design dramatically more energy-efficient technologies.

Illustration of a glowing black box emitting digital particles that form into a 3D model of an electrical grid infrastructure, set against a background of binary code and data visualizations.

Researchers at Oak Ridge National Laboratory have developed a modeling method that uses machine learning to accurately simulate electric grid behavior while protecting proprietary equipment details. The approach overcomes a key barrier to accurate grid modeling, helping utilities plan for future demand and prevent blackouts. 

 

Research scientist Daniel Jacobson is standing with his arms crossed with a dark black backdrop

Daniel Jacobson, distinguished research scientist in the Biosciences Division at ORNL, has been elected a Fellow of the American Institute for Medical and Biological Engineering, or AIMBE, for his achievements in computational biology. 

Researcher is sitting in bio lab surrounded with plants

Dave Weston studies how microorganisms influence plant health and stress tolerance, using the Advanced Plant Phenotyping Laboratory to accelerate research on plant-microbe interactions and develop resilient crops for advanced fuels, chemicals and 

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. 

Six images fanned out across the right side of the page with the first page showcasing the report cover. To the right hand side is a green oak leaf.

A workshop led by scientists at ORNL sketched a road map toward a longtime goal: development of autonomous, or self-driving, next-generation research laboratories. 

Large group of over 30 students gather in conference room for a presentation, seated with their laptops

Not only did ORNL take home top honors at the 2024 International Conference for High Performance Computing, Networking, Storage, and Analysis (SC24), but the lab’s computing staff also shared career advice and expertise with students eager to enter the world of supercomputing.

Neus Domingo Marimon, ORNL scientist, poses for a photo in black with hair down

Neus Domingo Marimon, leader of the Functional Atomic Force Microscopy group at the Center for Nanophase Materials Sciences of ORNL, has been elevated to senior member of the Institute of Electrical and Electronics Engineers.

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.