![Sphere that has the top right fourth removed (exposed) Colors from left are orange, dark blue with orange dots, light blue with horizontal lines, then black. Inside the exposure is green and black with boxes.](/sites/default/files/styles/featured_square_large/public/2024-06/slicer.jpg?h=56311bf6&itok=bCZz09pJ)
Filter News
Area of Research
- (-) Neutron Science (12)
- (-) Supercomputing (35)
- Advanced Manufacturing (2)
- Biology and Environment (15)
- Clean Energy (47)
- Computer Science (5)
- Electricity and Smart Grid (1)
- Fuel Cycle Science and Technology (1)
- Functional Materials for Energy (2)
- Fusion and Fission (13)
- Fusion Energy (1)
- Isotope Development and Production (1)
- Isotopes (3)
- Materials (40)
- Materials for Computing (5)
- National Security (12)
- Nuclear Science and Technology (9)
- Quantum information Science (2)
- Sensors and Controls (1)
News Type
News Topics
- (-) Artificial Intelligence (15)
- (-) Climate Change (3)
- (-) Composites (1)
- (-) Energy Storage (8)
- (-) Machine Learning (6)
- (-) Nuclear Energy (3)
- (-) Quantum Science (15)
- (-) Security (4)
- 3-D Printing/Advanced Manufacturing (6)
- Advanced Reactors (1)
- Big Data (3)
- Bioenergy (7)
- Biology (7)
- Biomedical (9)
- Biotechnology (1)
- Buildings (2)
- Chemical Sciences (3)
- Computer Science (34)
- Coronavirus (8)
- Cybersecurity (7)
- Decarbonization (3)
- Environment (7)
- Exascale Computing (10)
- Frontier (15)
- Fusion (1)
- Grid (4)
- High-Performance Computing (17)
- Isotopes (1)
- Materials (15)
- Materials Science (16)
- Microscopy (5)
- Molten Salt (1)
- Nanotechnology (11)
- National Security (5)
- Neutron Science (43)
- Partnerships (1)
- Physics (11)
- Quantum Computing (5)
- Simulation (4)
- Software (1)
- Space Exploration (1)
- Summit (15)
- Sustainable Energy (7)
- Transportation (3)
Media Contacts
OAK RIDGE, Tenn., Feb. 12, 2019—A team of researchers from the Department of Energy’s Oak Ridge and Los Alamos National Laboratories has partnered with EPB, a Chattanooga utility and telecommunications company, to demonstrate the effectiveness of metro-scale quantum key distribution (QKD).
![Using as much as 50 percent lignin by weight, a new composite material created at ORNL is well suited for use in 3D printing. Using as much as 50 percent lignin by weight, a new composite material created at ORNL is well suited for use in 3D printing.](/sites/default/files/styles/list_page_thumbnail/public/2018-P09551.jpg?itok=q7Ri01Qb)
Scientists at the Department of Energy’s Oak Ridge National Laboratory have created a recipe for a renewable 3D printing feedstock that could spur a profitable new use for an intractable biorefinery byproduct: lignin.
![Graphical representation of a deuteron, the bound state of a proton (red) and a neutron (blue). Credit: Andy Sproles/Oak Ridge National Laboratory, U.S. Dept. of Energy. Graphical representation of a deuteron, the bound state of a proton (red) and a neutron (blue). Credit: Andy Sproles/Oak Ridge National Laboratory, U.S. Dept. of Energy.](/sites/default/files/styles/list_page_thumbnail/public/news/images/deuteron%5B4%5D.jpg?itok=hEV9C82i)
Scientists at the Department of Energy’s Oak Ridge National Laboratory are the first to successfully simulate an atomic nucleus using a quantum computer. The results, published in Physical Review Letters, demonstrate the ability of quantum systems to compute nuclear ph...
![ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system. ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.](/sites/default/files/styles/list_page_thumbnail/public/news/images/RAvENNA%20release%20pic.png?itok=2bDpK5Mo)
A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the