Filter News
Area of Research
- Advanced Manufacturing (2)
- Biological Systems (1)
- Biology and Environment (28)
- Building Technologies (1)
- Clean Energy (50)
- Climate and Environmental Systems (1)
- Computational Biology (2)
- Computational Engineering (2)
- Computer Science (12)
- Electricity and Smart Grid (2)
- Fusion and Fission (19)
- Fusion Energy (10)
- Isotopes (8)
- Materials (27)
- Materials for Computing (6)
- Mathematics (1)
- National Security (22)
- Neutron Science (17)
- Nuclear Science and Technology (28)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Quantum information Science (4)
- Sensors and Controls (1)
- Supercomputing (68)
News Type
News Topics
- (-) Biomedical (39)
- (-) Computer Science (119)
- (-) Grid (43)
- (-) Machine Learning (31)
- (-) Nuclear Energy (71)
- (-) Space Exploration (22)
- (-) Transformational Challenge Reactor (3)
- 3-D Printing/Advanced Manufacturing (66)
- Advanced Reactors (21)
- Artificial Intelligence (58)
- Big Data (37)
- Bioenergy (64)
- Biology (74)
- Biotechnology (13)
- Buildings (36)
- Chemical Sciences (30)
- Clean Water (27)
- Climate Change (69)
- Composites (15)
- Coronavirus (28)
- Critical Materials (13)
- Cybersecurity (17)
- Decarbonization (51)
- Education (1)
- Emergency (2)
- Energy Storage (59)
- Environment (143)
- Exascale Computing (25)
- Fossil Energy (4)
- Frontier (24)
- Fusion (37)
- High-Performance Computing (53)
- Hydropower (11)
- Irradiation (2)
- Isotopes (31)
- ITER (5)
- Materials (75)
- Materials Science (76)
- Mathematics (6)
- Mercury (10)
- Microelectronics (2)
- Microscopy (31)
- Molten Salt (6)
- Nanotechnology (28)
- National Security (36)
- Net Zero (9)
- Neutron Science (74)
- Partnerships (16)
- Physics (31)
- Polymers (17)
- Quantum Computing (23)
- Quantum Science (39)
- Renewable Energy (1)
- Security (11)
- Simulation (36)
- Software (1)
- Statistics (1)
- Summit (36)
- Sustainable Energy (87)
- Transportation (62)
Media Contacts
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 U.S. Department of Energy’s Oak Ridge Leadership Computing Facility has informed the recipients of high-performance computing time through the SummitPLUS allocation program, which extends the operation of the Summit supercomputer through October 2024.
A team of computational scientists at ORNL has generated and released datasets of unprecedented scale that provide the ultraviolet visible spectral properties of over 10 million organic molecules.
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.
A 19-member team of scientists from across the national laboratory complex won the Association for Computing Machinery’s 2023 Gordon Bell Special Prize for Climate Modeling for developing a model that uses the world’s first exascale supercomputer to simulate decades’ worth of cloud formations.
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.