Filter News
Area of Research
- Advanced Manufacturing (22)
- Biology and Environment (29)
- Building Technologies (1)
- Clean Energy (92)
- Computational Engineering (1)
- Computer Science (4)
- Fuel Cycle Science and Technology (1)
- Fusion and Fission (4)
- Fusion Energy (1)
- Isotopes (1)
- Materials (51)
- Materials for Computing (9)
- National Security (41)
- Neutron Science (11)
- Nuclear Science and Technology (4)
- Quantum information Science (2)
- Supercomputing (38)
News Topics
- (-) 3-D Printing/Advanced Manufacturing (128)
- (-) Big Data (62)
- (-) Microscopy (51)
- (-) National Security (73)
- Advanced Reactors (35)
- Artificial Intelligence (102)
- Bioenergy (92)
- Biology (102)
- Biomedical (62)
- Biotechnology (24)
- Buildings (67)
- Chemical Sciences (74)
- Clean Water (31)
- Climate Change (106)
- Composites (30)
- Computer Science (199)
- Coronavirus (46)
- Critical Materials (29)
- Cybersecurity (35)
- Decarbonization (85)
- Education (5)
- Element Discovery (1)
- Emergency (2)
- Energy Storage (112)
- Environment (201)
- Exascale Computing (44)
- Fossil Energy (6)
- Frontier (46)
- Fusion (59)
- Grid (67)
- High-Performance Computing (94)
- Hydropower (11)
- Irradiation (3)
- Isotopes (57)
- ITER (7)
- Machine Learning (51)
- Materials (150)
- Materials Science (149)
- Mathematics (10)
- Mercury (12)
- Microelectronics (4)
- Molten Salt (9)
- Nanotechnology (60)
- Net Zero (14)
- Neutron Science (140)
- Nuclear Energy (111)
- Partnerships (51)
- Physics (64)
- Polymers (33)
- Quantum Computing (39)
- Quantum Science (73)
- Renewable Energy (2)
- Security (26)
- Simulation (53)
- Software (1)
- Space Exploration (25)
- Statistics (3)
- Summit (61)
- Sustainable Energy (130)
- Transformational Challenge Reactor (7)
- Transportation (99)
Media Contacts
ORNL and The University of Toledo have entered into a memorandum of understanding for collaborative research.
In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly.
A team including Oak Ridge National Laboratory and University of Tennessee researchers demonstrated a novel 3D printing approach called Z-pinning that can increase the material’s strength and toughness by more than three and a half times compared to conventional additive manufacturing processes.
More than 6,000 veterans died by suicide in 2016, and from 2005 to 2016, the rate of veteran suicides in the United States increased by more than 25 percent.
Craig Blue, a program director at the Department of Energy’s Oak Ridge National Laboratory, has been elected a 2019 fellow for SME (formerly known as the Society for Manufacturing Engineers).
Using additive manufacturing, scientists experimenting with tungsten at Oak Ridge National Laboratory hope to unlock new potential of the high-performance heat-transferring material used to protect components from the plasma inside a fusion reactor. Fusion requires hydrogen isotopes to reach millions of degrees.
Using the Titan supercomputer at Oak Ridge National Laboratory, a team of astrophysicists created a set of galactic wind simulations of the highest resolution ever performed. The simulations will allow researchers to gather and interpret more accurate, detailed data that elucidates how galactic winds affect the formation and evolution of galaxies.
Researchers at Oak Ridge National Laboratory are taking inspiration from neural networks to create computers that mimic the human brain—a quickly growing field known as neuromorphic computing.
In the shifting landscape of global manufacturing, American ingenuity is once again giving U.S companies an edge with radical productivity improvements as a result of advanced materials and robotic systems developed at the Department of Energy’s Manufacturing Demonstration Facility (MDF) at Oak Ridge National Laboratory.
A study led by Oak Ridge National Laboratory explored the interface between the Department of Veterans Affairs’ healthcare data system and the data itself to detect the likelihood of errors and designed an auto-surveillance tool