Filter News
Area of Research
- Advanced Manufacturing (4)
- Biology and Environment (14)
- Clean Energy (25)
- Computational Biology (1)
- Computational Engineering (2)
- Computer Science (6)
- Electricity and Smart Grid (1)
- Functional Materials for Energy (1)
- Fusion and Fission (8)
- Fusion Energy (7)
- Isotope Development and Production (1)
- Isotopes (1)
- Materials (43)
- Materials for Computing (5)
- Mathematics (1)
- National Security (17)
- Neutron Science (101)
- Nuclear Science and Technology (15)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Supercomputing (48)
News Topics
- (-) Advanced Reactors (34)
- (-) Artificial Intelligence (94)
- (-) Irradiation (3)
- (-) Mathematics (9)
- (-) Neutron Science (131)
- 3-D Printing/Advanced Manufacturing (125)
- Big Data (58)
- Bioenergy (92)
- Biology (100)
- Biomedical (59)
- Biotechnology (22)
- Buildings (57)
- Chemical Sciences (66)
- Clean Water (30)
- Climate Change (101)
- Composites (28)
- Computer Science (193)
- Coronavirus (46)
- Critical Materials (28)
- Cybersecurity (35)
- Decarbonization (80)
- Education (4)
- Element Discovery (1)
- Emergency (2)
- Energy Storage (109)
- Environment (196)
- Exascale Computing (39)
- Fossil Energy (6)
- Frontier (44)
- Fusion (55)
- Grid (65)
- High-Performance Computing (88)
- Hydropower (11)
- Isotopes (53)
- ITER (7)
- Machine Learning (48)
- Materials (144)
- Materials Science (142)
- Mercury (12)
- Microelectronics (3)
- Microscopy (51)
- Molten Salt (8)
- Nanotechnology (60)
- National Security (65)
- Net Zero (14)
- Nuclear Energy (109)
- Partnerships (46)
- Physics (62)
- Polymers (33)
- Quantum Computing (35)
- Quantum Science (69)
- Renewable Energy (2)
- Security (24)
- Simulation (49)
- Software (1)
- Space Exploration (25)
- Statistics (3)
- Summit (59)
- Sustainable Energy (129)
- Transformational Challenge Reactor (7)
- Transportation (97)
Media Contacts
Two ORNL teams recently completed Cohort 18 of Energy I-Corps, an immersive two-month training program where the scientists define their technology’s value propositions, conduct stakeholder discovery interviews and develop viable market pathways.
Researchers at the Department of Energy’s Oak Ridge National Laboratory and partner institutions have launched a project to develop an innovative suite of tools that will employ machine learning algorithms for more effective cybersecurity analysis of the U.S. power grid.
The contract will be awarded to develop the newest high-performance computing system at the Oak Ridge Leadership Computing Facility.
A newly established internship between ORNL and Maryville College is bringing cybersecurity careers to a local liberal arts college. The internship was established by a Maryville College alumni who recently joined ORNL.
Oak Ridge National Laboratory scientists have developed a method leveraging artificial intelligence to accelerate the identification of environmentally friendly solvents for industrial carbon capture, biomass processing, rechargeable batteries and other applications.
In May, the Department of Energy’s Oak Ridge and Brookhaven national laboratories co-hosted the 15th annual International Particle Accelerator Conference, or IPAC, at the Music City Center in Nashville, Tennessee.
Prasanna Balaprakash, a national leader in artificial intelligence, or AI, spoke to some of the highest achieving students in the country at the National Science Bowl in Washington D.C.
ORNL researchers and communications specialists took part in the inaugural AI Expo for National Competitiveness in Washington D.C, May 7 and 8, to showcase and provide insight into how the lab is leading the way for utilizing the vast possibilities of AI.
John Lagergren, a staff scientist in Oak Ridge National Laboratory’s Plant Systems Biology group, is using his expertise in applied math and machine learning to develop neural networks to quickly analyze the vast amounts of data on plant traits amassed at ORNL’s Advanced Plant Phenotyping Laboratory.
Vanderbilt University and ORNL announced a partnership to develop training, testing and evaluation methods that will accelerate the Department of Defense’s adoption of AI-based systems in operational environments.