Filter News
Area of Research
- Advanced Manufacturing (1)
- Biological Systems (2)
- Biology and Environment (41)
- Clean Energy (17)
- Computational Engineering (1)
- Computer Science (4)
- Fuel Cycle Science and Technology (1)
- Fusion and Fission (17)
- Fusion Energy (13)
- Isotopes (1)
- Materials (11)
- Materials for Computing (1)
- National Security (28)
- Neutron Science (7)
- Nuclear Science and Technology (17)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Quantum information Science (1)
- Supercomputing (15)
News Type
News Topics
- (-) Advanced Reactors (21)
- (-) Bioenergy (63)
- (-) Fusion (37)
- (-) Machine Learning (31)
- (-) Molten Salt (6)
- (-) National Security (36)
- 3-D Printing/Advanced Manufacturing (64)
- Artificial Intelligence (56)
- Big Data (36)
- Biology (73)
- Biomedical (39)
- Biotechnology (13)
- Buildings (35)
- Chemical Sciences (28)
- Clean Water (27)
- Climate Change (67)
- Composites (14)
- Computer Science (119)
- Coronavirus (28)
- Critical Materials (13)
- Cybersecurity (17)
- Decarbonization (51)
- Education (1)
- Emergency (2)
- Energy Storage (59)
- Environment (143)
- Exascale Computing (25)
- Fossil Energy (4)
- Frontier (24)
- Grid (43)
- High-Performance Computing (53)
- Hydropower (11)
- Irradiation (2)
- Isotopes (30)
- ITER (5)
- Materials (74)
- Materials Science (74)
- Mathematics (6)
- Mercury (10)
- Microelectronics (2)
- Microscopy (31)
- Nanotechnology (28)
- Net Zero (9)
- Neutron Science (73)
- Nuclear Energy (70)
- Partnerships (14)
- Physics (30)
- Polymers (15)
- Quantum Computing (21)
- Quantum Science (37)
- Renewable Energy (1)
- Security (11)
- Simulation (35)
- Software (1)
- Space Exploration (22)
- Statistics (1)
- Summit (36)
- Sustainable Energy (86)
- Transformational Challenge Reactor (3)
- Transportation (62)
Media Contacts
Researchers tackling national security challenges at ORNL are upholding an 80-year legacy of leadership in all things nuclear. Today, they’re developing the next generation of technologies that will help reduce global nuclear risk and enable safe, secure, peaceful use of nuclear materials, worldwide.
Researchers at the Department of Energy’s Oak Ridge National Laboratory met recently at an AI Summit to better understand threats surrounding artificial intelligence. The event was part of ORNL’s mission to shape the future of safe and secure AI systems charged with our nation’s most precious data.
A team led by researchers at ORNL explored training strategies for one of the largest artificial intelligence models to date with help from the world’s fastest supercomputer. The findings could help guide training for a new generation of AI models for scientific research.
Researchers at ORNL are using a machine-learning model to answer ‘what if’ questions stemming from major events that impact large numbers of people. By simulating an event, such as extreme weather, researchers can see how people might respond to adverse situations, and those outcomes can be used to improve emergency planning.
To balance personal safety and research innovation, researchers at ORNL are employing a mathematical technique known as differential privacy to provide data privacy guarantees.
Scientists at Oak Ridge National Laboratory and six other Department of Energy national laboratories have developed a United States-based perspective for achieving net-zero carbon emissions.
The U.S. Environmental Protection Agency has approved the registration and use of a renewable gasoline blendstock developed by Vertimass LLC and ORNL that can significantly reduce the emissions profile of vehicles when added to conventional fuels.
Groundwater withdrawals are expected to peak in about one-third of the world’s basins by 2050, potentially triggering significant trade and agriculture shifts, a new analysis finds.
ORNL’s Erin Webb is co-leading a new Circular Bioeconomy Systems Convergent Research Initiative focused on advancing production and use of renewable carbon from Tennessee to meet societal needs.
Nuclear nonproliferation scientists at ORNL have published the Compendium of Uranium Raman and Infrared Experimental Spectra, a public database and analysis of structure-spectral relationships for uranium minerals. This first-of-its-kind dataset and corresponding analysis fill a key gap in the existing body of knowledge for mineralogists and actinide scientists.