Filter News
Area of Research
News Topics
- (-) Artificial Intelligence (3)
- (-) Composites (2)
- (-) Grid (1)
- (-) Nuclear Energy (1)
- (-) Security (1)
- 3-D Printing/Advanced Manufacturing (3)
- Advanced Reactors (1)
- Bioenergy (4)
- Biomedical (2)
- Computer Science (11)
- Cybersecurity (2)
- Energy Storage (1)
- Environment (1)
- Exascale Computing (1)
- Frontier (2)
- Fusion (2)
- Isotopes (3)
- Materials Science (7)
- Microscopy (2)
- Nanotechnology (4)
- Neutron Science (4)
- Physics (4)
- Quantum Science (5)
- Summit (5)
- Sustainable Energy (3)
- Transportation (1)
Media Contacts
The U.S. Department of Energy announced funding for 12 projects with private industry to enable collaboration with DOE national laboratories on overcoming challenges in fusion energy development.
ORNL and The University of Toledo have entered into a memorandum of understanding for collaborative research.
Processes like manufacturing aircraft parts, analyzing data from doctors’ notes and identifying national security threats may seem unrelated, but at the U.S. Department of Energy’s Oak Ridge National Laboratory, artificial intelligence is improving all of these tasks.
OAK RIDGE, Tenn., March 4, 2019—A team of researchers from the Department of Energy’s Oak Ridge National Laboratory Health Data Sciences Institute have harnessed the power of artificial intelligence to better match cancer patients with clinical trials.
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).
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.
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