Filter News
Area of Research
- (-) Computational Engineering (1)
- (-) Fusion and Fission (3)
- Advanced Manufacturing (7)
- Biology and Environment (19)
- Clean Energy (42)
- Computational Biology (1)
- Computer Science (6)
- Electricity and Smart Grid (1)
- Functional Materials for Energy (2)
- Fusion Energy (1)
- Isotopes (4)
- Materials (76)
- Materials Characterization (2)
- Materials for Computing (10)
- Materials Under Extremes (1)
- National Security (14)
- Neutron Science (20)
- Supercomputing (47)
News Topics
- (-) Artificial Intelligence (2)
- (-) Fossil Energy (1)
- (-) Materials (1)
- 3-D Printing/Advanced Manufacturing (3)
- Advanced Reactors (6)
- Big Data (1)
- Bioenergy (1)
- Biology (1)
- Biomedical (2)
- Buildings (1)
- Chemical Sciences (4)
- Clean Water (1)
- Climate Change (1)
- Composites (1)
- Computer Science (5)
- Critical Materials (1)
- Decarbonization (2)
- Energy Storage (4)
- Environment (3)
- Exascale Computing (1)
- Frontier (1)
- Fusion (22)
- Grid (2)
- High-Performance Computing (3)
- Isotopes (1)
- ITER (6)
- Machine Learning (1)
- Materials Science (4)
- Mathematics (1)
- Microscopy (1)
- Nanotechnology (1)
- Net Zero (1)
- Neutron Science (1)
- Nuclear Energy (26)
- Partnerships (3)
- Physics (1)
- Security (2)
- Simulation (3)
- Space Exploration (1)
- Summit (1)
- Sustainable Energy (4)
- Transportation (2)
Media Contacts
ORNL hosted its fourth Artificial Intelligence for Robust Engineering and Science, or AIRES, workshop from April 18-20. Over 100 attendees from government, academia and industry convened to identify research challenges and investment areas, carving the future of the discipline.
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.
The Department of Energy’s Office of Science has selected five Oak Ridge National Laboratory scientists for Early Career Research Program awards.
A study led by Oak Ridge National Laboratory explored the interface between the Department of Veterans Affairs’ healthcare data system and the data itself to detect the likelihood of errors and designed an auto-surveillance tool