Filter News
Area of Research
- Advanced Manufacturing (3)
- Biology and Environment (14)
- Clean Energy (53)
- Computational Engineering (2)
- Computer Science (6)
- Electricity and Smart Grid (3)
- Fuel Cycle Science and Technology (1)
- Functional Materials for Energy (1)
- Fusion and Fission (27)
- Fusion Energy (10)
- Isotope Development and Production (1)
- Isotopes (3)
- Materials (25)
- Mathematics (1)
- National Security (23)
- Neutron Science (6)
- Nuclear Science and Technology (36)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Quantum information Science (1)
- Sensors and Controls (1)
- Supercomputing (22)
News Topics
- (-) Grid (63)
- (-) Machine Learning (48)
- (-) Mathematics (8)
- (-) Microelectronics (3)
- (-) Nuclear Energy (109)
- 3-D Printing/Advanced Manufacturing (122)
- Advanced Reactors (34)
- Artificial Intelligence (91)
- Big Data (55)
- Bioenergy (92)
- Biology (99)
- Biomedical (58)
- Biotechnology (22)
- Buildings (57)
- Chemical Sciences (65)
- Clean Water (29)
- Climate Change (100)
- Composites (26)
- Computer Science (189)
- Coronavirus (46)
- Critical Materials (26)
- Cybersecurity (35)
- Decarbonization (80)
- Education (4)
- Element Discovery (1)
- Emergency (2)
- Energy Storage (109)
- Environment (195)
- Exascale Computing (37)
- Fossil Energy (6)
- Frontier (42)
- Fusion (55)
- High-Performance Computing (85)
- Hydropower (11)
- Irradiation (3)
- Isotopes (53)
- ITER (7)
- Materials (144)
- Materials Science (141)
- Mercury (12)
- Microscopy (51)
- Molten Salt (8)
- Nanotechnology (60)
- National Security (62)
- Net Zero (14)
- Neutron Science (131)
- Partnerships (44)
- Physics (61)
- Polymers (33)
- Quantum Computing (34)
- Quantum Science (69)
- Renewable Energy (2)
- Security (24)
- Simulation (48)
- Software (1)
- Space Exploration (25)
- Statistics (3)
- Summit (57)
- Sustainable Energy (126)
- Transformational Challenge Reactor (7)
- Transportation (97)
Media Contacts
In the age of easy access to generative AI software, user can take steps to stay safe. Suhas Sreehari, an applied mathematician, identifies misconceptions of generative AI that could lead to unintentionally bad outcomes for a user.
ORNL researchers are working to make EV charging more resilient by developing algorithms to deal with both internal and external triggers of charger failure. This will help charging stations remain available to traveling EV drivers, reducing range anxiety.
To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance.
Three staff members in ORNL’s Fusion and Fission Energy and Science Directorate have moved into newly established roles facilitating communication and program management with sponsors of the directorate’s Nuclear Energy and Fuel Cycle Division.
Scientists at ORNL are looking for a happy medium to enable the grid of the future, filling a gap between high and low voltages for power electronics technology that underpins the modern U.S. electric grid.
New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses.
In summer 2023, ORNL's Prasanna Balaprakash was invited to speak at a roundtable discussion focused on the importance of academic artificial intelligence research and development hosted by the White House Office of Science and Technology Policy and the U.S. National Science Foundation.
The 2023 top science achievements from HFIR and SNS feature a broad range of materials research published in high impact journals such as Nature and Advanced Materials.
Nuclear engineering students from the United States Military Academy and United States Naval Academy are working with researchers at ORNL to complete design concepts for a nuclear propulsion rocket to go to space in 2027 as part of the Defense Advanced Research Projects Agency DRACO program.
Lee's paper at the August conference in Bellevue, Washington, combined weather and power outage data for three states – Texas, Michigan and Hawaii – and used a machine learning model to predict how extreme weather such as thunderstorms, floods and tornadoes would affect local power grids and to estimate the risk for outages. The paper relied on data from the National Weather Service and the U.S. Department of Energy’s Environment for Analysis of Geo-Located Energy Information, or EAGLE-I, database.