Filter News
Area of Research
- (-) Nuclear Science and Technology (9)
- (-) Supercomputing (65)
- Advanced Manufacturing (21)
- Biology and Environment (32)
- Biology and Soft Matter (1)
- Building Technologies (1)
- Clean Energy (106)
- Computational Biology (1)
- Computational Engineering (1)
- Computer Science (7)
- Fusion and Fission (10)
- Fusion Energy (1)
- Materials (113)
- Materials for Computing (15)
- National Security (37)
- Neutron Science (101)
- Quantum information Science (2)
News Type
News Topics
- (-) 3-D Printing/Advanced Manufacturing (8)
- (-) Artificial Intelligence (34)
- (-) Chemical Sciences (5)
- (-) Cybersecurity (8)
- (-) Machine Learning (13)
- (-) Neutron Science (16)
- (-) Physics (9)
- (-) Polymers (2)
- Advanced Reactors (11)
- Big Data (18)
- Bioenergy (9)
- Biology (11)
- Biomedical (18)
- Biotechnology (2)
- Buildings (3)
- Climate Change (17)
- Computer Science (93)
- Coronavirus (14)
- Critical Materials (3)
- Decarbonization (4)
- Energy Storage (7)
- Environment (20)
- Exascale Computing (20)
- Frontier (26)
- Fusion (9)
- Grid (4)
- High-Performance Computing (34)
- Isotopes (6)
- Materials (13)
- Materials Science (18)
- Mathematics (1)
- Microscopy (7)
- Molten Salt (5)
- Nanotechnology (11)
- National Security (8)
- Net Zero (1)
- Nuclear Energy (36)
- Partnerships (1)
- Quantum Computing (19)
- Quantum Science (23)
- Security (5)
- Simulation (12)
- Software (1)
- Space Exploration (8)
- Summit (41)
- Sustainable Energy (9)
- Transformational Challenge Reactor (3)
- 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.
ORNL researchers used the nation’s fastest supercomputer to map the molecular vibrations of an important but little-studied uranium compound produced during the nuclear fuel cycle for results that could lead to a cleaner, safer world.
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.
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.
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.
A team of collaborators from ORNL, Google Inc., Snowflake Inc. and Ververica GmbH has tested a computing concept that could help speed up real-time processing of data that stream on mobile and other electronic devices.
A team led by the U.S. Department of Energy’s Oak Ridge National Laboratory demonstrated the viability of a “quantum entanglement witness” capable of proving the presence of entanglement between magnetic particles, or spins, in a quantum material.
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.