Filter News
Area of Research
- Advanced Manufacturing (14)
- Biological Systems (2)
- Biology and Environment (58)
- Building Technologies (1)
- Clean Energy (117)
- Computational Engineering (2)
- Computer Science (8)
- Electricity and Smart Grid (2)
- Energy Sciences (1)
- Fusion and Fission (5)
- Fusion Energy (1)
- Isotopes (1)
- Materials (29)
- Materials for Computing (7)
- Mathematics (1)
- National Security (18)
- Neutron Science (16)
- Nuclear Science and Technology (2)
- Quantum information Science (7)
- Sensors and Controls (1)
- Supercomputing (37)
News Type
News Topics
- (-) 3-D Printing/Advanced Manufacturing (73)
- (-) Big Data (44)
- (-) Bioenergy (67)
- (-) Clean Water (27)
- (-) Energy Storage (60)
- (-) Grid (46)
- (-) Machine Learning (33)
- (-) Mathematics (9)
- (-) Quantum Science (40)
- (-) Transformational Challenge Reactor (3)
- Advanced Reactors (21)
- Artificial Intelligence (61)
- Biology (78)
- Biomedical (40)
- Biotechnology (14)
- Buildings (38)
- Chemical Sciences (35)
- Climate Change (72)
- Composites (17)
- Computer Science (127)
- Coronavirus (28)
- Critical Materials (16)
- Cybersecurity (17)
- Decarbonization (55)
- Education (1)
- Emergency (2)
- Environment (147)
- Exascale Computing (28)
- Fossil Energy (5)
- Frontier (26)
- Fusion (40)
- High-Performance Computing (56)
- Hydropower (11)
- Irradiation (2)
- Isotopes (32)
- ITER (5)
- Materials (78)
- Materials Science (80)
- Mercury (10)
- Microelectronics (2)
- Microscopy (31)
- Molten Salt (6)
- Nanotechnology (28)
- National Security (42)
- Net Zero (10)
- Neutron Science (74)
- Nuclear Energy (74)
- Partnerships (19)
- Physics (33)
- Polymers (17)
- Quantum Computing (25)
- Renewable Energy (1)
- Security (12)
- Simulation (39)
- Software (1)
- Space Exploration (22)
- Statistics (2)
- Summit (37)
- Sustainable Energy (92)
- Transportation (62)
Media Contacts
In the age of easy access to generative AI software, user can take steps to stay safe. Suhas Sreehari, an applied mathematician, identifies misconceptions of generative AI that could lead to unintentionally bad outcomes for a user.
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 was front and center recently at one of the world’s largest optical networking conferences, the 2024 Optic Fiber Communication Conference and Exhibition, or OFC. ORNL researchers had major roles at the OFC 2024, a three-day event held in San Diego, California from March 26-28 which featured thousands of the world’s leading optical communications and networking professionals.
Scientists at ORNL have developed 3-D-printed collimator techniques that can be used to custom design collimators that better filter out noise during different types of neutron scattering experiments
ORNL scientists have determined how to avoid costly and potentially irreparable damage to large metallic parts fabricated through additive manufacturing, also known as 3D printing, that is caused by residual stress in the material.
An experiment by researchers at the Department of Energy’s Oak Ridge National Laboratory demonstrated advanced quantum-based cybersecurity can be realized in a deployed fiber link.
A team that included researchers at ORNL used a new twist on an old method to detect materials at some of the smallest amounts yet recorded. The results could lead to enhancements in security technology and aid the development of quantum sensors.
To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance.
ORNL climate modeling expertise contributed to a project that assessed global emissions of ammonia from croplands now and in a warmer future, while also identifying solutions tuned to local growing conditions.