Filter News
Area of Research
- (-) Supercomputing (42)
- Advanced Manufacturing (6)
- Biology and Environment (40)
- Clean Energy (61)
- Climate and Environmental Systems (4)
- Computational Biology (1)
- Computational Engineering (2)
- Computer Science (3)
- Fuel Cycle Science and Technology (1)
- Fusion and Fission (13)
- Fusion Energy (7)
- Isotope Development and Production (1)
- Isotopes (10)
- Materials (87)
- Materials Characterization (1)
- Materials for Computing (14)
- Materials Under Extremes (1)
- Mathematics (1)
- National Security (16)
- Neutron Science (67)
- Nuclear Science and Technology (20)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Quantum information Science (2)
- Transportation Systems (1)
News Type
News Topics
- (-) Cybersecurity (6)
- (-) Environment (7)
- (-) Isotopes (1)
- (-) Materials Science (7)
- (-) Neutron Science (7)
- (-) Nuclear Energy (2)
- (-) Physics (4)
- (-) Summit (20)
- 3-D Printing/Advanced Manufacturing (3)
- Advanced Reactors (1)
- Artificial Intelligence (13)
- Big Data (5)
- Bioenergy (6)
- Biology (5)
- Biomedical (9)
- Biotechnology (1)
- Buildings (1)
- Chemical Sciences (4)
- Climate Change (5)
- Computer Science (47)
- Coronavirus (7)
- Critical Materials (3)
- Decarbonization (1)
- Energy Storage (6)
- Exascale Computing (8)
- Frontier (13)
- Fusion (1)
- Grid (3)
- High-Performance Computing (14)
- Machine Learning (6)
- Materials (9)
- Microscopy (5)
- Molten Salt (1)
- Nanotechnology (6)
- National Security (5)
- Partnerships (1)
- Polymers (2)
- Quantum Computing (9)
- Quantum Science (13)
- Security (4)
- Simulation (2)
- Space Exploration (2)
- Sustainable Energy (6)
- Transportation (3)
Media Contacts
Using neutrons to see the additive manufacturing process at the atomic level, scientists have shown that they can measure strain in a material as it evolves and track how atoms move in response to stress.
Researchers from Oak Ridge National Laboratory and Northeastern University modeled how extreme conditions in a changing climate affect the land’s ability to absorb atmospheric carbon — a key process for mitigating human-caused emissions. They found that 88% of Earth’s regions could become carbon emitters by the end of the 21st century.
Laboratory Director Thomas Zacharia presented five Director’s Awards during Saturday night's annual Awards Night event hosted by UT-Battelle, which manages ORNL for the Department of Energy.
University of Pennsylvania researchers called on computational systems biology expertise at Oak Ridge National Laboratory to analyze large datasets of single-cell RNA sequencing from skin samples afflicted with atopic dermatitis.
A study led by researchers at ORNL used the nation’s fastest supercomputer to close in on the answer to a central question of modern physics that could help conduct development of the next generation of energy technologies.
More than 50 current employees and recent retirees from ORNL received Department of Energy Secretary’s Honor Awards from Secretary Jennifer Granholm in January as part of project teams spanning the national laboratory system. The annual awards recognized 21 teams and three individuals for service and contributions to DOE’s mission and to the benefit of the nation.
A world-leading researcher in solid electrolytes and sophisticated electron microscopy methods received Oak Ridge National Laboratory’s top science honor today for her work in developing new materials for batteries. The announcement was made during a livestreamed Director’s Awards event hosted by ORNL Director Thomas Zacharia.
A team of collaborators from ORNL, Google Inc., Snowflake Inc. and Ververica GmbH has tested a computing concept that could help speed up real-time processing of data that stream on mobile and other electronic devices.
The U.S. Department of Energy’s Office of Science announced allocations of supercomputer access to 51 high-impact computational science projects for 2022 through its Innovative and Novel Computational Impact on Theory and Experiment, or INCITE, program.
The Department of Energy’s Oak Ridge National Laboratory has licensed its award-winning artificial intelligence software system, the Multinode Evolutionary Neural Networks for Deep Learning, to General Motors for use in vehicle technology and design.