![Wavy photo representing high performance computing](/sites/default/files/styles/featured_square_large/public/2024-09/ORNL%20BG03%5B84%5D.jpg?h=50f775af&itok=2RkMJksc)
Filter News
Area of Research
- (-) Sensors and Controls (1)
- (-) Supercomputing (36)
- Advanced Manufacturing (5)
- Biological Systems (1)
- Biology and Environment (27)
- Clean Energy (71)
- Computational Biology (2)
- Computational Engineering (1)
- Computer Science (2)
- Electricity and Smart Grid (3)
- Fuel Cycle Science and Technology (2)
- Functional Materials for Energy (1)
- Fusion and Fission (29)
- Fusion Energy (10)
- Isotope Development and Production (1)
- Isotopes (12)
- Materials (39)
- Materials for Computing (5)
- National Security (44)
- Neutron Science (20)
- Nuclear Science and Technology (38)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Quantum information Science (1)
News Topics
- (-) Biomedical (17)
- (-) Grid (6)
- (-) National Security (8)
- (-) Nuclear Energy (4)
- (-) Space Exploration (3)
- 3-D Printing/Advanced Manufacturing (5)
- Advanced Reactors (1)
- Artificial Intelligence (36)
- Big Data (19)
- Bioenergy (9)
- Biology (11)
- Biotechnology (2)
- Buildings (4)
- Chemical Sciences (5)
- Climate Change (17)
- Computer Science (95)
- Coronavirus (14)
- Critical Materials (3)
- Cybersecurity (8)
- Decarbonization (5)
- Energy Storage (8)
- Environment (21)
- Exascale Computing (23)
- Frontier (29)
- Fusion (1)
- High-Performance Computing (39)
- Isotopes (2)
- Machine Learning (14)
- Materials (15)
- Materials Science (16)
- Mathematics (1)
- Microscopy (7)
- Molten Salt (1)
- Nanotechnology (11)
- Net Zero (1)
- Neutron Science (13)
- Partnerships (1)
- Physics (8)
- Polymers (2)
- Quantum Computing (19)
- Quantum Science (24)
- Security (6)
- Simulation (15)
- Software (1)
- Summit (42)
- Sustainable Energy (10)
- Transportation (6)
Media Contacts
![The ORNL researchers’ findings may enable better detection of uranium tetrafluoride hydrate, a little-studied byproduct of the nuclear fuel cycle, and better understanding of how environmental conditions influence the chemical behavior of fuel cycle materials. Credit: Kevin Pastoor/Colorado School of Mines](/sites/default/files/styles/list_page_thumbnail/public/2022-05/UF4%20hydrate.png?h=d318f057&itok=spT-Dg48)
ORNL researchers used the nation’s fastest supercomputer to map the molecular vibrations of an important but little-studied uranium compound produced during the nuclear fuel cycle for results that could lead to a cleaner, safer world.
![ORNL, VA and Harvard researchers developed a sparse matrix full of anonymized information on what is thought to be the largest cohort of healthcare data used for this type of research in the U.S. The matrix can be probed with different methods, such as KESER, to gain new insights into human health. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-04/2022-G00330_KESER%20Illustration_0.jpg?h=1cb48fc4&itok=c6ZuDdDg)
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.
![Genetic analysis revealed connections between inflammatory activity and development of atomic dermatitis, according to researchers from the UPenn School of Medicine, the Perelman School of Medicine, and Oak Ridge National Laboratory. Credit: Kang Ko/UPenn](/sites/default/files/styles/list_page_thumbnail/public/2022-02/Graves-AD_0.jpg?h=46d8a70d&itok=77AW7Swv)
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.
![Mars Rover 2020](/sites/default/files/styles/list_page_thumbnail/public/2019-03/Mars_0.jpg?h=c44fcfa1&itok=gSstQOJO)
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.
![An ORNL-led team studied the SARS-CoV-2 spike protein in the trimer state, shown here, to pinpoint structural transitions that could be disrupted to destabilize the protein and negate its harmful effects. Credit: Debsindhu Bhowmik/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/sars_cov_2_bk.png?h=05c2797f&itok=jQ2D9aTr)
To explore the inner workings of severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, researchers from ORNL developed a novel technique.
![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.
![An ORNL-led team comprising researchers from multiple DOE national laboratories is using artificial intelligence and computational screening techniques – in combination with experimental validation – to identify and design five promising drug therapy approaches to target the SARS-CoV-2 virus. Credit: Michelle Lehman/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-06/frame1.png?h=d1cb525d&itok=51pwBWyP)
An ORNL-led team comprising researchers from multiple DOE national laboratories is using artificial intelligence and computational screening techniques – in combination with experimental validation – to identify and design five promising drug therapy approaches to target the SARS-CoV-2 virus.
![ORNL researchers are developing a method to print low-cost, high-fidelity, customizable sensors for monitoring power grid equipment. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-02/SAW%20sensors%202021-P01084_0.jpg?h=8f9cfe54&itok=H3Fe6A_G)
A method developed at Oak Ridge National Laboratory to print high-fidelity, passive sensors for energy applications can reduce the cost of monitoring critical power grid assets.
![Six ORNL scientists have been elected as fellows to the American Association for the Advancement of Science, or AAAS. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2020-11/AAASfellows.jpg?h=d761c044&itok=opKRkA17)
Six ORNL scientists have been elected as fellows to the American Association for the Advancement of Science, or AAAS.
![Frontier supercomputer](/sites/default/files/styles/list_page_thumbnail/public/2023-01/frontier_0.jpg?h=a1e1a043&itok=J3IM_Xeh)
A multi-institutional team, led by a group of investigators at Oak Ridge National Laboratory, has been studying various SARS-CoV-2 protein targets, including the virus’s main protease. The feat has earned the team a finalist nomination for the Association of Computing Machinery, or ACM, Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research.