Filter News
Area of Research
News Type
News Topics
- (-) Advanced Reactors (8)
- (-) Big Data (21)
- (-) Clean Water (14)
- (-) Cybersecurity (14)
- (-) Environment (100)
- (-) Exascale Computing (24)
- (-) Grid (23)
- (-) Isotopes (26)
- (-) Space Exploration (12)
- 3-D Printing/Advanced Manufacturing (35)
- Artificial Intelligence (45)
- Bioenergy (49)
- Biology (57)
- Biomedical (28)
- Biotechnology (10)
- Buildings (17)
- Chemical Sciences (21)
- Climate Change (47)
- Composites (6)
- Computer Science (80)
- Coronavirus (17)
- Critical Materials (1)
- Decarbonization (43)
- Education (1)
- Emergency (2)
- Energy Storage (28)
- Fossil Energy (4)
- Frontier (23)
- Fusion (28)
- High-Performance Computing (42)
- Hydropower (5)
- ITER (2)
- Machine Learning (21)
- Materials (40)
- Materials Science (43)
- Mathematics (5)
- Mercury (7)
- Microelectronics (2)
- Microscopy (20)
- Molten Salt (1)
- Nanotechnology (16)
- National Security (33)
- Net Zero (8)
- Neutron Science (47)
- Nuclear Energy (52)
- Partnerships (15)
- Physics (27)
- Polymers (8)
- Quantum Computing (19)
- Quantum Science (29)
- Renewable Energy (1)
- Security (10)
- Simulation (29)
- Software (1)
- Summit (30)
- Sustainable Energy (43)
- Transformational Challenge Reactor (3)
- Transportation (27)
Media Contacts
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.
Jack Orebaugh, a forensic anthropology major at the University of Tennessee, Knoxville, has a big heart for families with missing loved ones. When someone disappears in an area of dense vegetation, search and recovery efforts can be difficult, especially when a missing person’s last location is unknown. Recognizing the agony of not knowing what happened to a family or friend, Orebaugh decided to use his internship at the Department of Energy’s Oak Ridge National Laboratory to find better ways to search for lost and deceased people using cameras and drones.
The 21st Symposium on Separation Science and Technology for Energy Applications, Oct. 23-26 at the Embassy Suites by Hilton West in Knoxville, attracted 109 researchers, including some from Austria and the Czech Republic. Besides attending many technical sessions, they had the opportunity to tour the Graphite Reactor, High Flux Isotope Reactor and both supercomputers at ORNL.
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.
Scientists from more than a dozen institutions have completed a first-of-its-kind high-resolution assessment of carbon dioxide removal potential in the United States, charting a path to achieve a net-zero greenhouse gas economy by 2050.
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.
A team of eight scientists won the Association for Computing Machinery’s 2023 Gordon Bell Prize for their study that used the world’s first exascale supercomputer to run one of the largest simulations of an alloy ever and achieve near-quantum accuracy.
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.