Filter News
Area of Research
- (-) Fusion Energy (2)
- (-) National Security (4)
- Biology and Environment (17)
- Clean Energy (35)
- Climate and Environmental Systems (2)
- Computational Biology (1)
- Computer Science (2)
- Electricity and Smart Grid (1)
- Energy Sciences (1)
- Functional Materials for Energy (2)
- Fusion and Fission (3)
- Isotopes (3)
- Materials (25)
- Materials for Computing (3)
- Neutron Science (13)
- Nuclear Science and Technology (3)
- Quantum information Science (1)
- Supercomputing (14)
News Topics
- (-) Computer Science (5)
- (-) Energy Storage (1)
- (-) Environment (1)
- (-) Neutron Science (2)
- (-) Sustainable Energy (2)
- Advanced Reactors (3)
- Big Data (1)
- Biology (1)
- Climate Change (1)
- Coronavirus (1)
- Cybersecurity (2)
- Frontier (1)
- Fusion (3)
- Grid (1)
- Materials Science (1)
- National Security (4)
- Nuclear Energy (3)
- Physics (1)
- Summit (2)
Media Contacts
Three ORNL scientists have been elected fellows of the American Association for the Advancement of Science, or AAAS, the world’s largest general scientific society and publisher of the Science family of journals.
Six ORNL scientists have been elected as fellows to the American Association for the Advancement of Science, or AAAS.
Combining expertise in physics, applied math and computing, Oak Ridge National Laboratory scientists are expanding the possibilities for simulating electromagnetic fields that underpin phenomena in materials design and telecommunications.
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.
The prospect of simulating a fusion plasma is a step closer to reality thanks to a new computational tool developed by scientists in fusion physics, computer science and mathematics at ORNL.
To better determine the potential energy cost savings among connected homes, researchers at Oak Ridge National Laboratory developed a computer simulation to more accurately compare energy use on similar weather days.