Filter News
Area of Research
- Biological Systems (1)
- Biology and Environment (53)
- Biology and Soft Matter (1)
- Clean Energy (35)
- Climate and Environmental Systems (1)
- Computational Biology (1)
- Fusion and Fission (5)
- Isotopes (3)
- Materials (12)
- Materials for Computing (4)
- National Security (6)
- Neutron Science (7)
- Nuclear Science and Technology (5)
- Quantum information Science (1)
- Supercomputing (33)
News Type
News Topics
- (-) Advanced Reactors (8)
- (-) Bioenergy (51)
- (-) Biomedical (30)
- (-) Climate Change (52)
- (-) Frontier (26)
- (-) Molten Salt (1)
- (-) Net Zero (8)
- (-) Polymers (8)
- (-) Transportation (27)
- 3-D Printing/Advanced Manufacturing (42)
- Artificial Intelligence (50)
- Big Data (29)
- Biology (60)
- Biotechnology (12)
- Buildings (21)
- Chemical Sciences (27)
- Clean Water (15)
- Composites (8)
- Computer Science (87)
- Coronavirus (17)
- Critical Materials (5)
- Cybersecurity (14)
- Decarbonization (46)
- Education (1)
- Emergency (2)
- Energy Storage (30)
- Environment (105)
- Exascale Computing (28)
- Fossil Energy (4)
- Fusion (31)
- Grid (26)
- High-Performance Computing (47)
- Hydropower (5)
- Isotopes (30)
- ITER (2)
- Machine Learning (22)
- Materials (44)
- Materials Science (47)
- Mathematics (7)
- Mercury (7)
- Microelectronics (3)
- Microscopy (20)
- Nanotechnology (16)
- National Security (45)
- Neutron Science (49)
- Nuclear Energy (56)
- Partnerships (19)
- Physics (31)
- Quantum Computing (22)
- Quantum Science (31)
- Renewable Energy (1)
- Security (12)
- Simulation (33)
- Software (1)
- Space Exploration (12)
- Statistics (1)
- Summit (31)
- Sustainable Energy (48)
- Transformational Challenge Reactor (3)
Media Contacts
ORNL’s Erin Webb is co-leading a new Circular Bioeconomy Systems Convergent Research Initiative focused on advancing production and use of renewable carbon from Tennessee to meet societal needs.
A first-ever dataset bridging molecular information about the poplar tree microbiome to ecosystem-level processes has been released by a team of DOE scientists led by ORNL. The project aims to inform research regarding how natural systems function, their vulnerability to a changing climate and ultimately how plants might be engineered for better performance as sources of bioenergy and natural carbon storage.
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.
ORNL scientists and researchers attended the annual American Geophysical Union meeting and came away inspired for the year ahead in geospatial, earth and climate science.
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.
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.
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.