Filter News
Area of Research
- Advanced Manufacturing (2)
- Biology and Environment (33)
- Building Technologies (1)
- Clean Energy (107)
- Climate and Environmental Systems (1)
- Computational Biology (1)
- Computational Engineering (3)
- Computer Science (17)
- Electricity and Smart Grid (1)
- Energy Sciences (1)
- Functional Materials for Energy (2)
- Fusion and Fission (10)
- Fusion Energy (2)
- Isotopes (26)
- Materials (119)
- Materials for Computing (18)
- Mathematics (1)
- National Security (27)
- Neutron Science (36)
- Nuclear Science and Technology (9)
- Quantum information Science (9)
- Supercomputing (145)
News Topics
- (-) Artificial Intelligence (87)
- (-) Computer Science (184)
- (-) Critical Materials (25)
- (-) Energy Storage (108)
- (-) Frontier (41)
- (-) Isotopes (49)
- (-) Physics (60)
- (-) Polymers (31)
- (-) Quantum Science (66)
- 3-D Printing/Advanced Manufacturing (116)
- Advanced Reactors (34)
- Big Data (50)
- Bioenergy (88)
- Biology (96)
- Biomedical (58)
- Biotechnology (21)
- Buildings (55)
- Chemical Sciences (60)
- Clean Water (29)
- Climate Change (95)
- Composites (25)
- Coronavirus (46)
- Cybersecurity (35)
- Decarbonization (75)
- Education (4)
- Element Discovery (1)
- Emergency (2)
- Environment (192)
- Exascale Computing (36)
- Fossil Energy (5)
- Fusion (53)
- Grid (61)
- High-Performance Computing (83)
- Hydropower (11)
- Irradiation (3)
- ITER (7)
- Machine Learning (46)
- Materials (141)
- Materials Science (137)
- Mathematics (6)
- Mercury (12)
- Microelectronics (2)
- Microscopy (51)
- Molten Salt (8)
- Nanotechnology (60)
- National Security (59)
- Net Zero (12)
- Neutron Science (130)
- Nuclear Energy (105)
- Partnerships (40)
- Quantum Computing (31)
- Renewable Energy (2)
- Security (24)
- Simulation (45)
- Software (1)
- Space Exploration (25)
- Statistics (3)
- Summit (57)
- Sustainable Energy (122)
- Transformational Challenge Reactor (7)
- Transportation (94)
Media Contacts
Associate Technician Sean Hollander is the keeper of the Fundamental Neutron Physics Beamline, which is operated by the Physics Division at the Spallation Neutron Source at ORNL, where scientists use neutrons to study all manner of matter.
ORNL scientists develop a sample holder that tumbles powdered photochemical materials within a neutron beamline — exposing more of the material to light for increased photo-activation and better photochemistry data capture.
ORNL researchers used electron-beam additive manufacturing to 3D-print the first complex, defect-free tungsten parts with complex geometries.
Scientists have uncovered the properties of a rare earth element that was first discovered 80 years ago at the very same laboratory, opening a new pathway for the exploration of elements critical in modern technology, from medicine to space travel.
Students from the first class of ORNL and Pellissippi State Community College's joint Chemical Radiation Technology Pathway toured isotope facilities at ORNL.
Researchers tackling national security challenges at ORNL are upholding an 80-year legacy of leadership in all things nuclear. Today, they’re developing the next generation of technologies that will help reduce global nuclear risk and enable safe, secure, peaceful use of nuclear materials, worldwide.
Researchers at ORNL are developing battery technologies to fight climate change in two ways, by expanding the use of renewable energy and capturing airborne carbon dioxide.
A team of researchers including a member of the Quantum Science Center at ORNL has published a review paper on the state of the field of Majorana research. The paper primarily describes four major platforms that are capable of hosting these particles, as well as the progress made over the past decade in this area.
Researchers at the Department of Energy’s Oak Ridge National Laboratory met recently at an AI Summit to better understand threats surrounding artificial intelligence. The event was part of ORNL’s mission to shape the future of safe and secure AI systems charged with our nation’s most precious data.
A team led by researchers at ORNL explored training strategies for one of the largest artificial intelligence models to date with help from the world’s fastest supercomputer. The findings could help guide training for a new generation of AI models for scientific research.