Filter News
Area of Research
- (-) Computer Science (15)
- (-) National Security (35)
- (-) Quantum information Science (9)
- Advanced Manufacturing (22)
- Biology and Environment (43)
- Building Technologies (2)
- Clean Energy (112)
- Climate and Environmental Systems (1)
- Computational Biology (2)
- Computational Engineering (3)
- Electricity and Smart Grid (1)
- Fuel Cycle Science and Technology (1)
- Functional Materials for Energy (1)
- Fusion and Fission (28)
- Fusion Energy (11)
- Isotope Development and Production (1)
- Isotopes (4)
- Materials (62)
- Materials for Computing (13)
- Mathematics (1)
- Neutron Science (29)
- Nuclear Science and Technology (37)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Supercomputing (133)
News Topics
- (-) 3-D Printing/Advanced Manufacturing (2)
- (-) Big Data (10)
- (-) Computer Science (37)
- (-) Machine Learning (16)
- (-) Nuclear Energy (5)
- (-) Quantum Science (11)
- (-) Summit (3)
- Advanced Reactors (1)
- Artificial Intelligence (17)
- Bioenergy (4)
- Biology (5)
- Biomedical (2)
- Biotechnology (1)
- Buildings (2)
- Chemical Sciences (2)
- Climate Change (5)
- Coronavirus (2)
- Cybersecurity (21)
- Decarbonization (2)
- Energy Storage (3)
- Environment (6)
- Exascale Computing (2)
- Frontier (1)
- Fusion (1)
- Grid (8)
- High-Performance Computing (6)
- Materials (2)
- Materials Science (4)
- Microscopy (2)
- Nanotechnology (2)
- National Security (34)
- Neutron Science (4)
- Partnerships (4)
- Physics (2)
- Security (11)
- Simulation (1)
- Sustainable Energy (5)
- Transportation (2)
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.
![Earth Day](/sites/default/files/styles/list_page_thumbnail/public/2022-04/Earth%20image.png?h=8f74817f&itok=5rQ_su9Z)
Tackling the climate crisis and achieving an equitable clean energy future are among the biggest challenges of our time.
![ORNL scientists created a new microbial trait mapping process that improves on classical protoplast fusion techniques to identify the genes that trigger desirable genetic traits like improved biomass processing. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy. Reprinted with the permission of Oxford University Press, publisher of Nucleic Acids Research](/sites/default/files/styles/list_page_thumbnail/public/2022-04/Nucleic%20Cover%20Illustration.jpg?h=4a9d1e17&itok=iw81emAt)
ORNL scientists had a problem mapping the genomes of bacteria to better understand the origins of their physical traits and improve their function for bioenergy production.
![Dongarra in 2019 with Oak Ridge National Laboratory's Summit supercomputer](/sites/default/files/styles/list_page_thumbnail/public/2022-03/I%29%20Dongarra_IBM_Summit_Superomputer.jpeg?h=4bf1c8f5&itok=9sM8m0Iz)
A force within the supercomputing community, Jack Dongarra developed software packages that became standard in the industry, allowing high-performance computers to become increasingly more powerful in recent decades.
![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 open-source code developed by an ORNL-led team could provide new insights into the everyday operation of the nation’s power grid. Credit: Pixabay](/sites/default/files/styles/list_page_thumbnail/public/2021-10/digitization-gef50ab16f_1920_0.jpg?h=e5aec6c8&itok=55oFYLLz)
Oak Ridge National Laboratory, University of Tennessee and University of Central Florida researchers released a new high-performance computing code designed to more efficiently examine power systems and identify electrical grid disruptions, such as
![Nicholas Peters and Raphael Pooser](/sites/default/files/styles/list_page_thumbnail/public/2021-09/2021-P07657_2.jpg?h=00f06886&itok=p2qp4F74)
Of the $61 million recently announced by the U.S. Department of Energy for quantum information science studies, $17.5 million will fund research at DOE’s Oak Ridge National Laboratory. These projects will help build the foundation for the quantum internet, advance quantum entanglement capabilities — which involve sharing information through paired particles of light called photons — and develop next-generation quantum sensors.
![ORNL’s particle entanglement machine is a precursor to the device that researchers at the University of Oklahoma are building, which will produce entangled quantum particles for quantum sensing to detect underground pipeline leaks. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-07/IMG_20170706_154618586AK_0.jpg?h=61873cd7&itok=0OWbsNbu)
To minimize potential damage from underground oil and gas leaks, Oak Ridge National Laboratory is co-developing a quantum sensing system to detect pipeline leaks more quickly.
![An algorithm developed and field-tested by ORNL researchers uses machine learning to maintain homeowners’ preferred temperatures year-round while minimizing energy costs. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-07/2019-P07408_2.jpg?h=8f9cfe54&itok=jBvKdqIv)
Oak Ridge National Laboratory researchers designed and field-tested an algorithm that could help homeowners maintain comfortable temperatures year-round while minimizing utility costs.