Filter News
Area of Research
- Advanced Manufacturing (6)
- Biology and Environment (13)
- Clean Energy (41)
- Computational Engineering (1)
- Computer Science (8)
- Electricity and Smart Grid (1)
- Fusion and Fission (3)
- Fusion Energy (1)
- Isotopes (9)
- Materials (25)
- National Security (13)
- Neutron Science (6)
- Nuclear Science and Technology (2)
- Quantum information Science (3)
- Supercomputing (35)
News Topics
- (-) 3-D Printing/Advanced Manufacturing (35)
- (-) Artificial Intelligence (38)
- (-) Big Data (17)
- (-) Grid (21)
- (-) Isotopes (12)
- (-) Machine Learning (20)
- (-) Microscopy (12)
- (-) Quantum Science (19)
- (-) Space Exploration (8)
- Advanced Reactors (10)
- Bioenergy (31)
- Biology (29)
- Biomedical (12)
- Biotechnology (7)
- Buildings (14)
- Chemical Sciences (24)
- Clean Water (10)
- Climate Change (31)
- Composites (8)
- Computer Science (58)
- Coronavirus (4)
- Critical Materials (6)
- Cybersecurity (14)
- Decarbonization (30)
- Education (3)
- Emergency (1)
- Energy Storage (29)
- Environment (62)
- Exascale Computing (17)
- Fossil Energy (2)
- Frontier (21)
- Fusion (14)
- High-Performance Computing (33)
- Hydropower (3)
- Irradiation (2)
- Materials (59)
- Materials Science (36)
- Mathematics (2)
- Mercury (3)
- Microelectronics (2)
- Molten Salt (2)
- Nanotechnology (13)
- National Security (21)
- Net Zero (5)
- Neutron Science (50)
- Nuclear Energy (38)
- Partnerships (24)
- Physics (20)
- Polymers (6)
- Quantum Computing (12)
- Renewable Energy (2)
- Security (5)
- Simulation (29)
- Software (1)
- Summit (18)
- Sustainable Energy (25)
- Transportation (30)
Media Contacts
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.
Effective Dec. 4, Gina Tourassi will assume responsibilities as associate laboratory director for the Computing and Computational Sciences Directorate at the Department of Energy’s Oak Ridge National Laboratory.
Digital twins are exactly what they sound like: virtual models of physical reality that continuously update to reflect changes in the real world.
Four scientists affiliated with ORNL were named Battelle Distinguished Inventors during the lab’s annual Innovation Awards on Dec. 1 in recognition of being granted 14 or more United States patents.
ORNL is home to the world's fastest exascale supercomputer, Frontier, which was built in part to facilitate energy-efficient and scalable AI-based algorithms and simulations.
ORNL has joined a global consortium of scientists from federal laboratories, research institutes, academia and industry to address the challenges of building large-scale artificial intelligence systems and advancing trustworthy and reliable AI for
Scientists at ORNL used their knowledge of complex ecosystem processes, energy systems, human dynamics, computational science and Earth-scale modeling to inform the nation’s latest National Climate Assessment, which draws attention to vulnerabilities and resilience opportunities in every region of the country.
A team of researchers associated with the Quantum Science Center headquartered at the Department of Energy's Oak Ridge National Laboratory has confirmed the presence of quantum spin liquid behavior in a new material with a triangular lattice, KYbSe2.
The world’s first exascale supercomputer will help scientists peer into the future of global climate change and open a window into weather patterns that could affect the world a generation from now.
Researchers at ORNL have been leading a project to understand how a high-altitude electromagnetic pulse, or EMP, could threaten power plants.