Filter News
Area of Research
- Advanced Manufacturing (14)
- Biology and Environment (26)
- Building Technologies (1)
- Clean Energy (110)
- Computational Engineering (2)
- Computer Science (8)
- Electricity and Smart Grid (2)
- Energy Sciences (1)
- Fusion and Fission (5)
- Fusion Energy (1)
- Isotopes (4)
- Materials (30)
- Materials for Computing (6)
- Mathematics (1)
- National Security (17)
- Neutron Science (15)
- Nuclear Science and Technology (5)
- Quantum information Science (7)
- Sensors and Controls (1)
- Supercomputing (37)
News Type
News Topics
- (-) 3-D Printing/Advanced Manufacturing (74)
- (-) Big Data (47)
- (-) Clean Water (28)
- (-) Energy Storage (61)
- (-) Grid (47)
- (-) Machine Learning (35)
- (-) Quantum Science (42)
- (-) Space Exploration (22)
- Advanced Reactors (21)
- Artificial Intelligence (65)
- Bioenergy (67)
- Biology (78)
- Biomedical (42)
- Biotechnology (15)
- Buildings (43)
- Chemical Sciences (38)
- Climate Change (76)
- Composites (17)
- Computer Science (129)
- Coronavirus (28)
- Critical Materials (17)
- Cybersecurity (17)
- Decarbonization (58)
- Education (2)
- Emergency (2)
- Environment (150)
- Exascale Computing (32)
- Fossil Energy (5)
- Frontier (28)
- Fusion (40)
- High-Performance Computing (60)
- Hydropower (11)
- Irradiation (2)
- Isotopes (36)
- ITER (5)
- Materials (81)
- Materials Science (83)
- Mathematics (9)
- Mercury (10)
- Microelectronics (3)
- Microscopy (31)
- Molten Salt (6)
- Nanotechnology (28)
- National Security (50)
- Net Zero (10)
- Neutron Science (80)
- Nuclear Energy (75)
- Partnerships (22)
- Physics (35)
- Polymers (17)
- Quantum Computing (27)
- Renewable Energy (1)
- Security (13)
- Simulation (41)
- Software (1)
- Statistics (2)
- Summit (39)
- Sustainable Energy (93)
- Transformational Challenge Reactor (3)
- Transportation (63)
Media Contacts
Technology Transfer staff from Department of Energy’s Oak Ridge National Laboratory attended the 2024 Consumer Electronics Show, or CES, in Las Vegas, Jan. 8–12.
ORNL’s successes in QIS and its forward-looking strategy were recently recognized in the form of three funding awards that will help ensure the laboratory remains a leader in advancing quantum computers and networks.
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.
Electric vehicles can drive longer distances if their lithium-ion batteries deliver more energy in a lighter package. A prime weight-loss candidate is the current collector, a component that often adds 10% to the weight of a battery cell without contributing energy.
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.
Researchers at ORNL became the first to 3D-print large rotating steam turbine blades for generating energy in power plants.
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.
On Nov. 1, about 250 employees at Oak Ridge National Laboratory gathered in person and online for Quantum on the Quad, an event designed to collect input for a quantum roadmap currently in development. This document will guide the laboratory's efforts in quantum science and technology, including strategies for expanding its expertise to all facets of the field.
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.