Filter News
Area of Research
- Advanced Manufacturing (22)
- Biology and Environment (19)
- Building Technologies (1)
- Clean Energy (118)
- Computational Engineering (1)
- Computer Science (3)
- Electricity and Smart Grid (3)
- Functional Materials for Energy (1)
- Fusion and Fission (6)
- Fusion Energy (1)
- Materials (30)
- Materials for Computing (4)
- Mathematics (1)
- National Security (9)
- Neutron Science (6)
- Nuclear Science and Technology (4)
- Quantum information Science (1)
- Sensors and Controls (1)
- Supercomputing (32)
News Topics
- (-) 3-D Printing/Advanced Manufacturing (122)
- (-) Exascale Computing (37)
- (-) Grid (63)
- (-) Mathematics (8)
- Advanced Reactors (34)
- Artificial Intelligence (91)
- Big Data (55)
- Bioenergy (92)
- Biology (99)
- Biomedical (58)
- Biotechnology (22)
- Buildings (57)
- Chemical Sciences (65)
- Clean Water (29)
- Climate Change (100)
- Composites (26)
- Computer Science (189)
- Coronavirus (46)
- Critical Materials (26)
- Cybersecurity (35)
- Decarbonization (80)
- Education (4)
- Element Discovery (1)
- Emergency (2)
- Energy Storage (109)
- Environment (195)
- Fossil Energy (6)
- Frontier (42)
- Fusion (55)
- High-Performance Computing (85)
- Hydropower (11)
- Irradiation (3)
- Isotopes (53)
- ITER (7)
- Machine Learning (48)
- Materials (144)
- Materials Science (141)
- Mercury (12)
- Microelectronics (3)
- Microscopy (51)
- Molten Salt (8)
- Nanotechnology (60)
- National Security (62)
- Net Zero (14)
- Neutron Science (131)
- Nuclear Energy (109)
- Partnerships (44)
- Physics (61)
- Polymers (33)
- Quantum Computing (34)
- Quantum Science (69)
- Renewable Energy (2)
- Security (24)
- Simulation (48)
- Software (1)
- Space Exploration (25)
- Statistics (3)
- Summit (57)
- Sustainable Energy (126)
- Transformational Challenge Reactor (7)
- Transportation (97)
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.
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.
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.
Canan Karakaya, a R&D Staff member in the Chemical Process Scale-Up group at ORNL, was inspired to become a chemical engineer after she experienced a magical transformation that turned ammonia gas into ammonium nitrate, turning a liquid into white flakes gently floating through the air.
Scientists at ORNL are looking for a happy medium to enable the grid of the future, filling a gap between high and low voltages for power electronics technology that underpins the modern U.S. electric grid.
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.
Researchers at ORNL became the first to 3D-print large rotating steam turbine blades for generating energy in power plants.
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.