Filter News
Area of Research
- (-) Clean Energy (12)
- (-) Supercomputing (14)
- Advanced Manufacturing (1)
- Biology and Environment (17)
- Computational Biology (1)
- Computer Science (1)
- Electricity and Smart Grid (1)
- Functional Materials for Energy (1)
- Fusion and Fission (3)
- Materials (8)
- Materials for Computing (3)
- National Security (7)
- Neutron Science (1)
News Topics
- (-) Big Data (3)
- (-) Machine Learning (5)
- (-) Nanotechnology (3)
- (-) Summit (7)
- (-) Sustainable Energy (12)
- 3-D Printing/Advanced Manufacturing (7)
- Advanced Reactors (2)
- Artificial Intelligence (8)
- Bioenergy (5)
- Biology (6)
- Biomedical (4)
- Buildings (13)
- Chemical Sciences (4)
- Clean Water (1)
- Climate Change (8)
- Composites (2)
- Computer Science (11)
- Coronavirus (5)
- Critical Materials (2)
- Cybersecurity (3)
- Decarbonization (13)
- Energy Storage (16)
- Environment (9)
- Exascale Computing (6)
- Fossil Energy (1)
- Frontier (7)
- Fusion (1)
- Grid (8)
- High-Performance Computing (8)
- Hydropower (1)
- Materials (14)
- Materials Science (8)
- Microscopy (4)
- National Security (4)
- Net Zero (1)
- Neutron Science (1)
- Nuclear Energy (2)
- Partnerships (5)
- Physics (1)
- Polymers (2)
- Quantum Computing (7)
- Quantum Science (4)
- Security (2)
- Simulation (5)
- Space Exploration (1)
- Transformational Challenge Reactor (1)
- Transportation (9)
Media Contacts
ORNL and the Tennessee Valley Authority, or TVA, are joining forces to advance decarbonization technologies from discovery through deployment through a new memorandum of understanding, or MOU.
A study led by researchers at ORNL used the nation’s fastest supercomputer to close in on the answer to a central question of modern physics that could help conduct development of the next generation of energy technologies.
To explore the inner workings of severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, researchers from ORNL developed a novel technique.
A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.