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Illustration of melting point of lithium chloride, which is shown with green and blue structures in two rows.

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. 

Two ORNL researchers inspect carbon fiber materials - one black rectangular sheet and one see-through sheet of film.

Researchers at ORNL have developed an innovative new technique using carbon nanofibers to enhance binding in carbon fiber and other fiber-reinforced polymer composites – an advance likely to improve structural materials for automobiles, airplanes and other applications that require lightweight and strong materials. 

ORNL researcher Van Graves examines a transparent cylindrical device he developed and tested at CERN in 2007 to demonstrate that a jet of liquid mercury could serve as a target for a neutrino factory or muon collider.

Van Graves, an engineering manager at ORNL, is celebrating 40 years of dedicated service leading a diverse range of prominent engineering projects at ORNL and internationally. 

Graphic depiction of a neutron star, which looks like orange beans inside a cage

Using the Frontier supercomputer, a team of researchers from the Massachusetts Institute of Technology conducted large-scale calculations to chart the isospin density of a neutron star across a range of conditions. Their work provides new insights into how pressure and density interact within neutron stars, offering important predictions about their inner workings.

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.

ORNL researcher Jesse Labbe is working with plants in a greenhouse. He is framed on all sides with bright green leaves

Jesse Labbé aims to leverage biology, computation and engineering to address societal challenges related to energy, national security and health, while enhancing U.S. competitiveness. Labbé emphasizes the importance of translating groundbreaking research into practical applications that have real-world impact.

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. 

 

Erica Prates is presenting to a group of attendees at Vandy workshop in a table conference room, standing next to a screen glowing in white

Scientists at the Department of Energy’s Oak Ridge National Laboratory recently welcomed Vanderbilt University colleagues for a symposium on basic science research, with a focus on potential collaborations in the biomedical and biotechnology spaces.

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.