Filter News
Area of Research
- (-) Nuclear Science and Technology (2)
- (-) Supercomputing (28)
- Advanced Manufacturing (2)
- Biology and Environment (14)
- Clean Energy (22)
- Computational Engineering (1)
- Computer Science (1)
- Fusion and Fission (4)
- Fusion Energy (1)
- Isotopes (1)
- Materials (26)
- Materials for Computing (6)
- National Security (11)
- Neutron Science (7)
- Quantum information Science (1)
News Topics
- (-) Advanced Reactors (2)
- (-) Climate Change (3)
- (-) Cybersecurity (6)
- (-) Microscopy (5)
- (-) Molten Salt (1)
- (-) Summit (14)
- (-) Transformational Challenge Reactor (1)
- 3-D Printing/Advanced Manufacturing (4)
- Artificial Intelligence (12)
- Big Data (1)
- Bioenergy (6)
- Biology (4)
- Biomedical (6)
- Biotechnology (1)
- Buildings (1)
- Chemical Sciences (3)
- Computer Science (32)
- Coronavirus (5)
- Decarbonization (1)
- Energy Storage (5)
- Environment (3)
- Exascale Computing (7)
- Frontier (12)
- Fusion (1)
- Grid (3)
- High-Performance Computing (11)
- Isotopes (3)
- Machine Learning (5)
- Materials (8)
- Materials Science (7)
- Nanotechnology (5)
- National Security (5)
- Neutron Science (8)
- Nuclear Energy (7)
- Partnerships (1)
- Physics (5)
- Quantum Computing (5)
- Quantum Science (10)
- Security (4)
- Simulation (1)
- Space Exploration (3)
- Sustainable Energy (5)
- Transportation (2)
Media Contacts
![A new method for analyzing climate models brings together information from various lines of evidence to represent Earth’s climate sensitivity. Credit: Jason Smith/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-11/climate-models.png?h=b655f2ac&itok=l5A4_3yJ)
Researchers from institutions including ORNL have created a new method for statistically analyzing climate models that projects future conditions with more fidelity.
![An AI-generated image representing atoms and artificial neural networks. Credit: Maxim Ziatdinov, ORNL](/sites/default/files/styles/list_page_thumbnail/public/2023-04/atoms3.jpg?h=ab622562&itok=dNMzrFw8)
Researchers at ORNL have developed a machine-learning inspired software package that provides end-to-end image analysis of electron and scanning probe microscopy images.
![Michelle Kidder received the lab’s Director’s Award for Outstanding Individual Accomplishment in Science and Technology for her decades-long work mentoring students, teachers and early-career staff. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-11/2018-P04785_0.png?h=7a8a8cdf&itok=hysTNqXX)
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.
![Oak Ridge National Laboratory’s software suite AutoBEM is being used in the architecture, city planning, real estate and home efficiency industries. Users take advantage of the suite’s energy modeling of almost all U.S. buildings. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-09/autobem_0_0.jpg?h=571559ce&itok=-YDymByQ)
Two years after ORNL provided a model of nearly every building in America, commercial partners are using the tool for tasks ranging from designing energy-efficient buildings and cities to linking energy efficiency to real estate value and risk.
![A smart approach to microscopy and imaging developed at Oak Ridge National Laboratory could drive discoveries in materials for future technologies. Credit: Adam Malin/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-05/PFC%20Surface%20v3%20300dpi_1.jpg?h=9c3ba2fc&itok=s8arZbEt)
Researchers at ORNL are teaching microscopes to drive discoveries with an intuitive algorithm, developed at the lab’s Center for Nanophase Materials Sciences, that could guide breakthroughs in new materials for energy technologies, sensing and computing.
![An international team of researchers used Summit to model spin, charge and pair-density waves in cuprates, a type of copper alloy, to explore the materials’ superconducting properties. The results revealed new insights into the relationships between these dynamics as superconductivity develops. Credit: Jason Smith/ORNL](/sites/default/files/styles/list_page_thumbnail/public/2022-02/MaierSpinBanner.png?h=ae114f5c&itok=rdZETb8v)
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.
![Miaofang Chi, a scientist in the Center for Nanophase Materials Sciences, received the 2021 Director’s Award for Outstanding Individual Accomplishment in Science and Technology. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-12/2021-P09692_0.jpg?h=9bbd619b&itok=4iANdQKl)
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.
![Watermarks, considered the most efficient mechanisms for tracking how complete streaming data processing is, allow new tasks to be processed immediately after prior tasks are completed. Image Credit: Nathan Armistead, ORNL](/sites/default/files/styles/list_page_thumbnail/public/2021-11/Watermarks%5B1%5D.jpg?h=f7cc716d&itok=Er5k0WwK)
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.
![INCITE_narrow_logo](/sites/default/files/styles/list_page_thumbnail/public/2021-11/incite_narrow_1.png?h=a08abdbb&itok=2O5LBHgQ)
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.
![Oak Ridge National Laboratory’s MENNDL AI software system can design thousands of neural networks in a matter of hours. One example uses a driving simulator to evaluate a network’s ability to perceive objects under various lighting conditions. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-04/CARLA%20MENNDL%20sim001_1.png?h=e2caa22a&itok=tvE9seMo)
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.