Filter News
Area of Research
- (-) Supercomputing (82)
- Advanced Manufacturing (1)
- Biological Systems (2)
- Biology and Environment (72)
- Clean Energy (134)
- Computational Biology (2)
- Computational Engineering (1)
- Computer Science (3)
- Electricity and Smart Grid (3)
- Energy Frontier Research Centers (1)
- Functional Materials for Energy (1)
- Fusion and Fission (7)
- Fusion Energy (1)
- Isotopes (27)
- Materials (81)
- Materials for Computing (15)
- National Security (24)
- Neutron Science (33)
- Nuclear Science and Technology (10)
- Quantum information Science (3)
- Sensors and Controls (2)
- Transportation Systems (2)
News Topics
- (-) Bioenergy (9)
- (-) Biomedical (17)
- (-) Grid (5)
- (-) Isotopes (2)
- (-) Molten Salt (1)
- (-) Nanotechnology (11)
- (-) Security (5)
- (-) Summit (42)
- (-) Transportation (6)
- 3-D Printing/Advanced Manufacturing (5)
- Advanced Reactors (1)
- Artificial Intelligence (36)
- Big Data (19)
- Biology (11)
- Biotechnology (2)
- Buildings (4)
- Chemical Sciences (5)
- Climate Change (17)
- Computer Science (95)
- Coronavirus (14)
- Critical Materials (3)
- Cybersecurity (8)
- Decarbonization (5)
- Energy Storage (8)
- Environment (21)
- Exascale Computing (23)
- Frontier (29)
- Fusion (1)
- High-Performance Computing (39)
- Machine Learning (14)
- Materials (15)
- Materials Science (16)
- Mathematics (1)
- Microscopy (7)
- National Security (8)
- Net Zero (1)
- Neutron Science (13)
- Nuclear Energy (4)
- Partnerships (1)
- Physics (8)
- Polymers (2)
- Quantum Computing (19)
- Quantum Science (24)
- Simulation (15)
- Software (1)
- Space Exploration (3)
- Sustainable Energy (10)
Media Contacts
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.
The U.S. Department of Energy’s Office of Science announced allocations of supercomputer access to 51 high-impact computational science projects for 2022 through its Innovative and Novel Computational Impact on Theory and Experiment, or INCITE, program.
The daily traffic congestion along the streets and interstate lanes of Chattanooga could be headed the way of the horse and buggy with help from ORNL researchers.
Improved data, models and analyses from ORNL scientists and many other researchers in the latest global climate assessment report provide new levels of certainty about what the future holds for the planet
An ORNL-led team comprising researchers from multiple DOE national laboratories is using artificial intelligence and computational screening techniques – in combination with experimental validation – to identify and design five promising drug therapy approaches to target the SARS-CoV-2 virus.
At the Department of Energy’s Oak Ridge National Laboratory, scientists use artificial intelligence, or AI, to accelerate the discovery and development of materials for energy and information technologies.
The Department of Energy’s Oak Ridge National Laboratory has licensed its award-winning artificial intelligence software system, the Multinode Evolutionary Neural Networks for Deep Learning, to General Motors for use in vehicle technology and design.
The U.S. Department of Energy’s Innovative and Novel Computational Impact on Theory and Experiment, or INCITE, program is seeking proposals for high-impact, computationally intensive research campaigns in a broad array of science, engineering and computer science domains.
Twenty-seven ORNL researchers Zoomed into 11 middle schools across Tennessee during the annual Engineers Week in February. East Tennessee schools throughout Oak Ridge and Roane, Sevier, Blount and Loudon counties participated, with three West Tennessee schools joining in.
Since the 1930s, scientists have been using particle accelerators to gain insights into the structure of matter and the laws of physics that govern our world.