Filter News
Area of Research
- (-) National Security (9)
- Advanced Manufacturing (2)
- Biological Systems (1)
- Biology and Environment (30)
- Clean Energy (19)
- Computational Biology (2)
- Computational Engineering (1)
- Computer Science (3)
- Fuel Cycle Science and Technology (1)
- Fusion and Fission (27)
- Fusion Energy (10)
- Isotope Development and Production (1)
- Isotopes (8)
- Materials (34)
- Materials for Computing (5)
- Neutron Science (22)
- Nuclear Science and Technology (38)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Quantum information Science (9)
- Supercomputing (47)
News Topics
- (-) Biomedical (2)
- (-) Biotechnology (1)
- (-) Nuclear Energy (5)
- (-) Quantum Science (1)
- 3-D Printing/Advanced Manufacturing (2)
- Advanced Reactors (1)
- Artificial Intelligence (12)
- Big Data (6)
- Bioenergy (3)
- Biology (5)
- Buildings (1)
- Chemical Sciences (2)
- Climate Change (5)
- Computer Science (19)
- Coronavirus (2)
- Cybersecurity (19)
- Decarbonization (2)
- Energy Storage (2)
- Environment (5)
- Exascale Computing (1)
- Frontier (1)
- Fusion (1)
- Grid (6)
- High-Performance Computing (4)
- Machine Learning (12)
- Materials (2)
- Materials Science (3)
- Nanotechnology (1)
- National Security (34)
- Neutron Science (4)
- Partnerships (4)
- Physics (1)
- Security (11)
- Simulation (1)
- Summit (2)
- Sustainable Energy (3)
- Transportation (2)
Media Contacts
![ORNL seismic researcher Chengping Chai placed seismic sensors on the ground at various distances from an ORNL nuclear reactor to learn whether they could detect its operating state. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-06/2023-P03398.jpg?h=3e43625b&itok=TXK8tthh)
Like most scientists, Chengping Chai is not content with the surface of things: He wants to probe beyond to learn what’s really going on. But in his case, he is literally building a map of the world beneath, using seismic and acoustic data that reveal when and where the earth moves.
![Stephen Dahunsi. Credit: Jason Richards/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-03/2018-P00115_Stephen%20Dahunsi.jpg?h=b6236d98&itok=lRQh92bt)
Stephen Dahunsi’s desire to see more countries safely deploy nuclear energy is personal. Growing up in Nigeria, he routinely witnessed prolonged electricity blackouts as a result of unreliable energy supplies. It’s a problem he hopes future generations won’t have to experience.
![State and Local Economic Development Award](/sites/default/files/styles/list_page_thumbnail/public/2023-01/FLCAward3_thumbnail.png?h=d1cb525d&itok=FKj_T8JY)
A partnership of ORNL, the Tennessee Department of Economic and Community Development, the Community Reuse Organization of East Tennessee and TVA that aims to attract nuclear energy-related firms to Oak Ridge has been recognized with a state and local economic development award from the Federal Laboratory Consortium.
![MDF Exterior](/sites/default/files/styles/list_page_thumbnail/public/2022-06/2021-p07609.jpg?h=be3e4b3a&itok=YfKK7Wy2)
ORNL scientists will present new technologies available for licensing during the annual Technology Innovation Showcase. The event is 9 a.m. to 3 p.m. Thursday, June 16, at the Manufacturing Demonstration Facility at ORNL’s Hardin Valley campus.
![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.
![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.
![Deborah Frincke, one of the nation’s preeminent computer scientists and cybersecurity experts, serves as associate laboratory director of ORNL’s National Security Science Directorate. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-05/Deborah%20Frincke%20profile_0.jpg?h=8caed45b&itok=0eTC4gMH)
Deborah Frincke, one of the nation’s preeminent computer scientists and cybersecurity experts, serves as associate laboratory director of ORNL’s National Security Science Directorate. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy
![Hector J. Santos-Villalobos, left, and Oscar A. Martinez](/sites/default/files/styles/list_page_thumbnail/public/2020-08/henaac20.jpg?h=158d9140&itok=-NxooIrE)
Two staff members at the Department of Energy’s Oak Ridge National Laboratory have received prestigious HENAAC and Luminary Awards from Great Minds in STEM, a nonprofit organization that focuses on promoting STEM careers in underserved
![ORNL staff members (from left) Ashley Shields, Michael Galloway, Ketan Maheshwari and Andrew Miskowiec are collaborating on a project focused on predicting and analyzing crystal structures of new uranium oxide phases. Credit: Jason Richards/ORNL](/sites/default/files/styles/list_page_thumbnail/public/2019-03/teamphotoforhighlight_0.jpg?h=a00326b7&itok=O4yDtVj6)
Scientists at the Department of Energy’s Oak Ridge National Laboratory are working to understand both the complex nature of uranium and the various oxide forms it can take during processing steps that might occur throughout the nuclear fuel cycle.