Filter News
Area of Research
- Advanced Manufacturing (3)
- Biology and Environment (7)
- Clean Energy (25)
- Computer Science (2)
- Energy Frontier Research Centers (1)
- Fuel Cycle Science and Technology (1)
- Fusion and Fission (1)
- Fusion Energy (1)
- Isotopes (1)
- Materials (27)
- Materials for Computing (2)
- National Security (4)
- Neutron Science (8)
- Nuclear Science and Technology (7)
- Quantum information Science (1)
- Sensors and Controls (1)
- Supercomputing (26)
News Topics
- (-) Artificial Intelligence (6)
- (-) Buildings (1)
- (-) Composites (3)
- (-) Computer Science (27)
- (-) Energy Storage (8)
- (-) Machine Learning (4)
- (-) Nanotechnology (15)
- (-) Nuclear Energy (10)
- (-) Quantum Science (13)
- (-) Security (6)
- (-) Sustainable Energy (16)
- 3-D Printing/Advanced Manufacturing (20)
- Advanced Reactors (5)
- Bioenergy (13)
- Biology (1)
- Biomedical (8)
- Biotechnology (1)
- Chemical Sciences (3)
- Clean Water (1)
- Climate Change (4)
- Coronavirus (9)
- Critical Materials (2)
- Cybersecurity (6)
- Decarbonization (1)
- Environment (13)
- Exascale Computing (2)
- Frontier (2)
- Fusion (3)
- Grid (2)
- High-Performance Computing (2)
- Isotopes (7)
- Materials (2)
- Materials Science (24)
- Microscopy (5)
- Molten Salt (1)
- National Security (2)
- Neutron Science (24)
- Physics (10)
- Polymers (4)
- Space Exploration (1)
- Summit (12)
- Transformational Challenge Reactor (2)
- Transportation (7)
Media Contacts
![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...
![Default image of ORNL entry sign](/sites/default/files/styles/list_page_thumbnail/public/2023-09/default-thumbnail.jpg?h=553c93cc&itok=N_Kd1DVR)
James Peery, who led critical national security programs at Sandia National Laboratories and held multiple leadership positions at Los Alamos National Laboratory before arriving at the Department of Energy’s Oak Ridge National Laboratory last year, has been named a...
![From left, Andrew Lupini and Juan Carlos Idrobo use ORNL’s new monochromated, aberration-corrected scanning transmission electron microscope, a Nion HERMES to take the temperatures of materials at the nanoscale. Image credit: Oak Ridge National Laboratory From left, Andrew Lupini and Juan Carlos Idrobo use ORNL’s new monochromated, aberration-corrected scanning transmission electron microscope, a Nion HERMES to take the temperatures of materials at the nanoscale. Image credit: Oak Ridge National Laboratory](/sites/default/files/styles/list_page_thumbnail/public/news/images/2018-P00413.jpg?itok=UKejk7r2)
A scientific team led by the Department of Energy’s Oak Ridge National Laboratory has found a new way to take the local temperature of a material from an area about a billionth of a meter wide, or approximately 100,000 times thinner than a human hair. This discove...
![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