Filter News
Area of Research
- (-) Fusion and Fission (1)
- (-) National Security (4)
- Biological Systems (1)
- Biology and Environment (21)
- Clean Energy (9)
- Computational Biology (2)
- Computational Engineering (1)
- Computer Science (1)
- Fusion Energy (1)
- Isotopes (5)
- Materials (8)
- Materials for Computing (3)
- Neutron Science (15)
- Nuclear Science and Technology (2)
- Supercomputing (52)
News Topics
- (-) Biomedical (3)
- (-) Summit (2)
- 3-D Printing/Advanced Manufacturing (5)
- Advanced Reactors (7)
- Artificial Intelligence (13)
- Big Data (6)
- Bioenergy (4)
- Biology (6)
- Biotechnology (1)
- Buildings (2)
- Chemical Sciences (6)
- Climate Change (5)
- Composites (1)
- Computer Science (21)
- Coronavirus (2)
- Critical Materials (1)
- Cybersecurity (19)
- Decarbonization (4)
- Energy Storage (6)
- Environment (7)
- Exascale Computing (2)
- Fossil Energy (1)
- Frontier (2)
- Fusion (23)
- Grid (8)
- High-Performance Computing (6)
- Isotopes (1)
- ITER (6)
- Machine Learning (12)
- Materials (3)
- Materials Science (7)
- Microscopy (1)
- Nanotechnology (2)
- National Security (34)
- Net Zero (1)
- Neutron Science (5)
- Nuclear Energy (31)
- Partnerships (7)
- Physics (2)
- Quantum Science (1)
- Security (13)
- Simulation (4)
- Space Exploration (1)
- Sustainable Energy (7)
- Transportation (4)
Media Contacts
ORNL scientists will present new technologies available for licensing during the annual Technology Innovation Showcase. The event is 9 a.m. to 3 p.m. Thursday, June 16, at the Manufacturing Demonstration Facility at ORNL’s Hardin Valley campus.
ORNL researchers used the nation’s fastest supercomputer to map the molecular vibrations of an important but little-studied uranium compound produced during the nuclear fuel cycle for results that could lead to a cleaner, safer world.
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.
A novel approach developed by scientists at ORNL can scan massive datasets of large-scale satellite images to more accurately map infrastructure – such as buildings and roads – in hours versus days.