Filter News
Area of Research
- Advanced Manufacturing (16)
- Biology and Environment (36)
- Building Technologies (1)
- Clean Energy (103)
- Computational Engineering (2)
- Computer Science (9)
- Electricity and Smart Grid (3)
- Fuel Cycle Science and Technology (1)
- Functional Materials for Energy (1)
- Fusion and Fission (5)
- Fusion Energy (1)
- Isotopes (1)
- Materials (53)
- Materials for Computing (12)
- Mathematics (1)
- National Security (38)
- Neutron Science (17)
- Nuclear Science and Technology (2)
- Quantum information Science (8)
- Sensors and Controls (1)
- Supercomputing (50)
News Type
News Topics
- (-) 3-D Printing/Advanced Manufacturing (85)
- (-) Big Data (42)
- (-) Clean Water (27)
- (-) Grid (46)
- (-) Machine Learning (34)
- (-) Nanotechnology (40)
- (-) National Security (45)
- (-) Polymers (22)
- (-) Quantum Science (44)
- Advanced Reactors (26)
- Artificial Intelligence (67)
- Bioenergy (71)
- Biology (81)
- Biomedical (43)
- Biotechnology (14)
- Buildings (42)
- Chemical Sciences (42)
- Climate Change (77)
- Composites (19)
- Computer Science (137)
- Coronavirus (32)
- Critical Materials (15)
- Cybersecurity (23)
- Decarbonization (56)
- Education (1)
- Emergency (2)
- Energy Storage (74)
- Environment (158)
- Exascale Computing (28)
- Fossil Energy (4)
- Frontier (28)
- Fusion (47)
- High-Performance Computing (65)
- Hydropower (11)
- Irradiation (3)
- Isotopes (40)
- ITER (6)
- Materials (107)
- Materials Science (97)
- Mathematics (7)
- Mercury (10)
- Microelectronics (2)
- Microscopy (39)
- Molten Salt (7)
- Net Zero (10)
- Neutron Science (86)
- Nuclear Energy (86)
- Partnerships (22)
- Physics (38)
- Quantum Computing (26)
- Renewable Energy (1)
- Security (15)
- Simulation (41)
- Software (1)
- Space Exploration (23)
- Statistics (1)
- Summit (38)
- Sustainable Energy (95)
- Transformational Challenge Reactor (4)
- Transportation (72)
Media Contacts
John Lagergren, a staff scientist in Oak Ridge National Laboratory’s Plant Systems Biology group, is using his expertise in applied math and machine learning to develop neural networks to quickly analyze the vast amounts of data on plant traits amassed at ORNL’s Advanced Plant Phenotyping Laboratory.
Momentum for manufacturing innovation in the United States got a boost during the inaugural MDF Innovation Days, held recently at the U.S. Department of Energy Manufacturing Demonstration Facility at Oak Ridge National Laboratory.
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.
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.
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.
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.
ORNL researchers have teamed up with other national labs to develop a free platform called Open Energy Data Initiative Solar Systems Integration Data and Modeling to better analyze the behavior of electric grids incorporating many solar projects.
Mohamad Zineddin hopes to establish an interdisciplinary center of excellence for nuclear security at ORNL, combining critical infrastructure assessment and protection, risk mitigation, leadership in nuclear security, education and training, nuclear security culture and resilience strategies and techniques.
Researchers at ORNL are using a machine-learning model to answer ‘what if’ questions stemming from major events that impact large numbers of people. By simulating an event, such as extreme weather, researchers can see how people might respond to adverse situations, and those outcomes can be used to improve emergency planning.
To balance personal safety and research innovation, researchers at ORNL are employing a mathematical technique known as differential privacy to provide data privacy guarantees.