Filter News
Area of Research
- (-) Materials (34)
- (-) National Security (11)
- Advanced Manufacturing (1)
- Biology and Environment (16)
- Clean Energy (43)
- Computational Biology (1)
- Computational Engineering (2)
- Computer Science (2)
- Electricity and Smart Grid (1)
- Fusion and Fission (12)
- Fusion Energy (8)
- Isotopes (3)
- Materials for Computing (6)
- Mathematics (1)
- Neutron Science (64)
- Nuclear Science and Technology (7)
- Quantum information Science (1)
- Sensors and Controls (2)
- Supercomputing (21)
News Type
News Topics
- (-) Biomedical (5)
- (-) Clean Water (1)
- (-) Fusion (5)
- (-) Grid (5)
- (-) Neutron Science (23)
- (-) Security (6)
- 3-D Printing/Advanced Manufacturing (20)
- Advanced Reactors (3)
- Artificial Intelligence (10)
- Big Data (2)
- Bioenergy (10)
- Biology (5)
- Buildings (3)
- Chemical Sciences (24)
- Climate Change (5)
- Composites (7)
- Computer Science (17)
- Coronavirus (4)
- Critical Materials (13)
- Cybersecurity (12)
- Decarbonization (5)
- Energy Storage (27)
- Environment (10)
- Exascale Computing (1)
- Frontier (2)
- High-Performance Computing (3)
- Isotopes (7)
- ITER (1)
- Machine Learning (6)
- Materials (50)
- Materials Science (54)
- Microscopy (18)
- Molten Salt (3)
- Nanotechnology (29)
- National Security (11)
- Net Zero (1)
- Nuclear Energy (7)
- Partnerships (11)
- Physics (16)
- Polymers (12)
- Quantum Computing (2)
- Quantum Science (11)
- Renewable Energy (1)
- Space Exploration (1)
- Summit (2)
- Sustainable Energy (12)
- Transformational Challenge Reactor (1)
- Transportation (12)
Media Contacts
Guided by machine learning, chemists at ORNL designed a record-setting carbonaceous supercapacitor material that stores four times more energy than the best commercial material.
Using neutrons to see the additive manufacturing process at the atomic level, scientists have shown that they can measure strain in a material as it evolves and track how atoms move in response to stress.
ORNL has been selected to lead an Energy Earthshot Research Center, or EERC, focused on developing chemical processes that use sustainable methods instead of burning fossil fuels to radically reduce industrial greenhouse gas emissions to stem climate change and limit the crisis of a rapidly warming planet.
The Department of Energy’s Oak Ridge National Laboratory announced the establishment of the Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making.
Scientist-inventors from ORNL will present seven new technologies during the Technology Innovation Showcase on Friday, July 14, from 8 a.m.–4 p.m. at the Joint Institute for Computational Sciences on ORNL’s campus.
Warming a crystal of the mineral fresnoite, ORNL scientists discovered that excitations called phasons carried heat three times farther and faster than phonons, the excitations that usually carry heat through a material.
A partnership of ORNL, the Tennessee Department of Economic and Community Development, the Community Reuse Organization of East Tennessee and TVA that aims to attract nuclear energy-related firms to Oak Ridge has been recognized with a state and local economic development award from the Federal Laboratory Consortium.
Three researchers at ORNL have been named ORNL Corporate Fellows in recognition of significant career accomplishments and continued leadership in their scientific fields.
While studying how bio-inspired materials might inform the design of next-generation computers, scientists at ORNL achieved a first-of-its-kind result that could have big implications for both edge computing and human health.
Although blockchain is best known for securing digital currency payments, researchers at the Department of Energy’s Oak Ridge National Laboratory are using it to track a different kind of exchange: It’s the first time blockchain has ever been used to validate communication among devices on the electric grid.