Filter News
Area of Research
- (-) Biological Systems (2)
- (-) Supercomputing (63)
- Advanced Manufacturing (8)
- Biology and Environment (72)
- Building Technologies (2)
- Clean Energy (131)
- Computational Biology (1)
- Computational Engineering (1)
- Computer Science (10)
- Electricity and Smart Grid (3)
- Energy Sciences (1)
- Functional Materials for Energy (1)
- Fusion and Fission (7)
- Fusion Energy (2)
- Materials (61)
- Materials for Computing (10)
- National Security (25)
- Neutron Science (15)
- Nuclear Science and Technology (5)
- Quantum information Science (3)
- Sensors and Controls (1)
News Topics
- (-) Artificial Intelligence (36)
- (-) Big Data (19)
- (-) Bioenergy (11)
- (-) Grid (5)
- (-) Microscopy (7)
- (-) Molten Salt (1)
- (-) Sustainable Energy (10)
- 3-D Printing/Advanced Manufacturing (5)
- Advanced Reactors (1)
- Biology (11)
- Biomedical (18)
- 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 (22)
- Frontier (28)
- Fusion (1)
- High-Performance Computing (38)
- Isotopes (1)
- Machine Learning (14)
- Materials (15)
- Materials Science (16)
- Mathematics (1)
- Nanotechnology (11)
- National Security (8)
- Net Zero (1)
- Neutron Science (13)
- Nuclear Energy (4)
- Partnerships (1)
- Physics (7)
- Polymers (2)
- Quantum Computing (19)
- Quantum Science (24)
- Security (5)
- Simulation (14)
- Software (1)
- Space Exploration (3)
- Summit (42)
- Transportation (6)
Media Contacts
Researchers at ORNL are teaching microscopes to drive discoveries with an intuitive algorithm, developed at the lab’s Center for Nanophase Materials Sciences, that could guide breakthroughs in new materials for energy technologies, sensing and computing.
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 study led by researchers at ORNL could help make materials design as customizable as point-and-click.
Tackling the climate crisis and achieving an equitable clean energy future are among the biggest challenges of our time.
A force within the supercomputing community, Jack Dongarra developed software packages that became standard in the industry, allowing high-performance computers to become increasingly more powerful in recent decades.
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.
A world-leading researcher in solid electrolytes and sophisticated electron microscopy methods received Oak Ridge National Laboratory’s top science honor today for her work in developing new materials for batteries. The announcement was made during a livestreamed Director’s Awards event hosted by ORNL Director Thomas Zacharia.
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.