Filter News
Area of Research
- (-) Clean Energy (76)
- Advanced Manufacturing (18)
- Biology and Environment (19)
- Building Technologies (1)
- Computational Biology (1)
- Computational Engineering (2)
- Computer Science (8)
- Fuel Cycle Science and Technology (1)
- Fusion and Fission (13)
- Fusion Energy (7)
- Isotope Development and Production (1)
- Isotopes (2)
- Materials (76)
- Materials Characterization (1)
- Materials for Computing (14)
- Materials Under Extremes (1)
- National Security (10)
- Neutron Science (27)
- Nuclear Science and Technology (17)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Quantum information Science (4)
- Supercomputing (47)
- Transportation Systems (1)
News Type
News Topics
- (-) 3-D Printing/Advanced Manufacturing (51)
- (-) Big Data (2)
- (-) Machine Learning (6)
- (-) Materials Science (20)
- (-) Nuclear Energy (5)
- (-) Polymers (10)
- (-) Quantum Science (1)
- (-) Summit (2)
- Advanced Reactors (3)
- Artificial Intelligence (5)
- Bioenergy (16)
- Biology (7)
- Biomedical (4)
- Biotechnology (3)
- Buildings (21)
- Chemical Sciences (11)
- Clean Water (5)
- Climate Change (12)
- Composites (14)
- Computer Science (18)
- Coronavirus (6)
- Critical Materials (8)
- Cybersecurity (3)
- Decarbonization (14)
- Energy Storage (47)
- Environment (28)
- Exascale Computing (2)
- Fossil Energy (1)
- Frontier (1)
- Fusion (1)
- Grid (24)
- High-Performance Computing (3)
- Hydropower (2)
- Isotopes (1)
- Materials (29)
- Mathematics (1)
- Mercury (2)
- Microscopy (6)
- Molten Salt (1)
- Nanotechnology (6)
- National Security (4)
- Net Zero (2)
- Neutron Science (7)
- Partnerships (8)
- Physics (1)
- Renewable Energy (1)
- Security (3)
- Simulation (2)
- Space Exploration (2)
- Statistics (1)
- Sustainable Energy (51)
- Transformational Challenge Reactor (3)
- Transportation (43)
Media Contacts
Oak Ridge National Laboratory researchers have developed a machine learning model that could help predict the impact pandemics such as COVID-19 have on fuel demand in the United States.
Five researchers at the Department of Energy’s Oak Ridge National Laboratory have been named ORNL Corporate Fellows in recognition of significant career accomplishments and continued leadership in their scientific fields.
Scientists at ORNL used neutron scattering and supercomputing to better understand how an organic solvent and water work together to break down plant biomass, creating a pathway to significantly improve the production of renewable
Researchers at Oak Ridge National Laboratory developed a method that uses machine learning to predict seasonal fire risk in Africa, where half of the world’s wildfire-related carbon emissions originate.
Oak Ridge National Laboratory scientists seeking the source of charge loss in lithium-ion batteries demonstrated that coupling a thin-film cathode with a solid electrolyte is a rapid way to determine the root cause.
Oak Ridge National Laboratory has licensed a novel method to 3D print components used in neutron instruments for scientific research to the ExOne Company, a leading maker of binder jet 3D printing technology.
Oak Ridge National Laboratory researchers have developed a thin film, highly conductive solid-state electrolyte made of a polymer and ceramic-based composite for lithium metal batteries.
Researchers at the Department of Energy’s Oak Ridge National Laboratory are refining their design of a 3D-printed nuclear reactor core, scaling up the additive manufacturing process necessary to build it, and developing methods
Researchers demonstrated that an additively manufactured hot stamping die can withstand up to 25,000 usage cycles, proving that this technique is a viable solution for production.
Researchers at the Department of Energy’s Oak Ridge National Laboratory have received five 2019 R&D 100 Awards, increasing the lab’s total to 221 since the award’s inception in 1963.