Quantum precision reached in modeling molten salt behavior
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 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.
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.
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.
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.
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.
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.
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.
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.
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.