Filter News
Area of Research
- (-) Supercomputing (64)
- Advanced Manufacturing (9)
- Biology and Environment (48)
- Building Technologies (2)
- Clean Energy (155)
- 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 (14)
- Fusion Energy (9)
- Isotopes (24)
- Materials (77)
- Materials for Computing (13)
- National Security (33)
- Neutron Science (104)
- Nuclear Science and Technology (21)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Quantum information Science (2)
- Sensors and Controls (1)
- Transportation Systems (2)
News Topics
- (-) Advanced Reactors (1)
- (-) Artificial Intelligence (36)
- (-) Grid (5)
- (-) Isotopes (1)
- (-) Machine Learning (14)
- (-) Neutron Science (13)
- (-) Sustainable Energy (10)
- (-) Transportation (6)
- 3-D Printing/Advanced Manufacturing (5)
- Big Data (19)
- Bioenergy (9)
- Biology (11)
- Biomedical (17)
- 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)
- Materials (15)
- Materials Science (16)
- Mathematics (1)
- Microscopy (7)
- Molten Salt (1)
- Nanotechnology (11)
- National Security (8)
- Net Zero (1)
- 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)
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.
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 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.
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.
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.