Filter News
Area of Research
- (-) Advanced Manufacturing (1)
- (-) National Security (12)
- (-) Neutron Science (14)
- Biological Systems (1)
- Biology and Environment (20)
- Building Technologies (3)
- Clean Energy (33)
- Computational Biology (2)
- Computational Engineering (1)
- Computer Science (6)
- Fusion and Fission (5)
- Fusion Energy (7)
- Isotopes (4)
- Materials (23)
- Materials for Computing (3)
- Nuclear Science and Technology (10)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Quantum information Science (7)
- Supercomputing (53)
News Type
News Topics
- (-) Advanced Reactors (1)
- (-) Biomedical (8)
- (-) Buildings (1)
- (-) Machine Learning (11)
- (-) Physics (2)
- (-) Quantum Science (3)
- (-) Summit (3)
- 3-D Printing/Advanced Manufacturing (16)
- Artificial Intelligence (11)
- Big Data (7)
- Bioenergy (5)
- Biology (4)
- Biotechnology (1)
- Chemical Sciences (2)
- Clean Water (2)
- Climate Change (4)
- Composites (3)
- Computer Science (18)
- Coronavirus (5)
- Cybersecurity (9)
- Decarbonization (3)
- Energy Storage (5)
- Environment (8)
- Fossil Energy (1)
- Fusion (1)
- Grid (5)
- High-Performance Computing (4)
- Materials (13)
- Materials Science (15)
- Mathematics (1)
- Microscopy (2)
- Nanotechnology (4)
- National Security (22)
- Neutron Science (56)
- Nuclear Energy (5)
- Polymers (1)
- Quantum Computing (1)
- Security (6)
- Simulation (1)
- Space Exploration (3)
- Sustainable Energy (5)
- Transportation (3)
Media Contacts
![ORNL researchers are establishing a digital thread of data, algorithms and workflows to produce a continuously updated model of earth systems.](/sites/default/files/styles/list_page_thumbnail/public/2023-11/MicrosoftTeams-image%20%2823%29_0.png?h=c6980913&itok=cK99Pg3y)
Digital twins are exactly what they sound like: virtual models of physical reality that continuously update to reflect changes in the real world.
![A small droplet of water is suspended in midair via an electrostatic levitator that lifts charged particles using an electric field that counteracts gravity. Credit: Iowa State University/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-11/droplet.png?h=ddb1ad0c&itok=3nblnUcm)
How do you get water to float in midair? With a WAND2, of course. But it’s hardly magic. In fact, it’s a scientific device used by scientists to study matter.
![The DEMAND single crystal diffractometer at the High Flux Isotope Reactor, or HFIR, is the latest neutron instrument at the Department of Energy’s Oak Ridge National Laboratory to be equipped with machine learning-assisted software, called ReTIA. Credit: Jeremy Rumsey/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-09/DEMAND%20thumbnail%20image_0.jpg?h=c673cd1c&itok=5YAVwaP6)
Neutron experiments can take days to complete, requiring researchers to work long shifts to monitor progress and make necessary adjustments. But thanks to advances in artificial intelligence and machine learning, experiments can now be done remotely and in half the time.
![Cody Lloyd stands in front of images of historical nuclear field testing. The green and red dots are the machine learning algorithm recognizing features in the image. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-08/2023-P05797_0.jpg?h=4a7d1ed4&itok=S8h_wvJX)
Cody Lloyd became a nuclear engineer because of his interest in the Manhattan Project, the United States’ mission to advance nuclear science to end World War II. As a research associate in nuclear forensics at ORNL, Lloyd now teaches computers to interpret data from imagery of nuclear weapons tests from the 1950s and early 1960s, bringing his childhood fascination into his career
![Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-08/2023-P06111_0.jpg?h=c6980913&itok=dgI-yVRh)
After completing a bachelor’s degree in biology, Toya Beiswenger didn’t intend to go into forensics. But almost two decades later, the nuclear security scientist at ORNL has found a way to appreciate the art of nuclear forensics.
![Two researchers standing back to back in a grassy area](/sites/default/files/styles/list_page_thumbnail/public/2023-07/CSJ_1716_updated.jpg?h=2dfa0735&itok=U-3yNm3M)
When geoinformatics engineering researchers at the Department of Energy’s Oak Ridge National Laboratory wanted to better understand changes in land areas and points of interest around the world, they turned to the locals — their data, at least.
![Image of outerspace](/sites/default/files/styles/list_page_thumbnail/public/2023-04/Dark%20Matter%20Thumbnail.png?h=c673cd1c&itok=vaZLUOBP)
Few things carry the same aura of mystery as dark matter. The name itself radiates secrecy, suggesting something hidden in the shadows of the Universe.
![Philipe Ambrozio Dias. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-11/2022-P09862.jpg?h=4a7d1ed4&itok=CblZ5Rj4)
Having lived on three continents spanning the world’s four hemispheres, Philipe Ambrozio Dias understands the difficulties of moving to a new place.
![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.