![White car (Porsche Taycan) with the hood popped is inside the building with an american flag on the wall.](/sites/default/files/styles/featured_square_large/public/2024-06/2024-P09317.jpg?h=8f9cfe54&itok=m6sQhZRq)
Filter News
Area of Research
News Topics
- (-) Advanced Reactors (6)
- (-) Artificial Intelligence (9)
- (-) Grid (4)
- (-) Machine Learning (4)
- (-) Molten Salt (1)
- (-) Quantum Science (5)
- 3-D Printing/Advanced Manufacturing (11)
- Big Data (7)
- Bioenergy (5)
- Biomedical (3)
- Biotechnology (1)
- Clean Water (4)
- Composites (1)
- Computer Science (26)
- Critical Materials (1)
- Cybersecurity (1)
- Energy Storage (7)
- Environment (14)
- Exascale Computing (1)
- Fusion (4)
- Materials Science (10)
- Mercury (1)
- Microscopy (2)
- Nanotechnology (3)
- Neutron Science (12)
- Nuclear Energy (15)
- Physics (3)
- Polymers (1)
- Space Exploration (4)
- Summit (6)
- Sustainable Energy (3)
- Transportation (10)
Media Contacts
![At the salt–metal interface, thermodynamic forces drive chromium from the bulk of a nickel alloy, leaving a porous, weakened layer. Impurities in the salt drive further corrosion of the structural material. Credit: Stephen Raiman/Oak Ridge National Labora At the salt–metal interface, thermodynamic forces drive chromium from the bulk of a nickel alloy, leaving a porous, weakened layer. Impurities in the salt drive further corrosion of the structural material. Credit: Stephen Raiman/Oak Ridge National Labora](/sites/default/files/styles/list_page_thumbnail/public/story%20tip%20image%20BW%20only.jpg?itok=Vbc0iTLt)
Oak Ridge National Laboratory scientists analyzed more than 50 years of data showing puzzlingly inconsistent trends about corrosion of structural alloys in molten salts and found one factor mattered most—salt purity.
![ORNL scientists used commuting behavior data from East Tennessee to demonstrate how machine learning models can easily accept new data, quickly re-train themselves and update predictions about commuting patterns. Credit: April Morton/Oak Ridge National La ORNL scientists used commuting behavior data from East Tennessee to demonstrate how machine learning models can easily accept new data, quickly re-train themselves and update predictions about commuting patterns. Credit: April Morton/Oak Ridge National La](/sites/default/files/styles/list_page_thumbnail/public/study_area_one_dest_2.jpg?itok=2cWFkQvW)
Oak Ridge National Laboratory geospatial scientists who study the movement of people are using advanced machine learning methods to better predict home-to-work commuting patterns.
![Symposium attendees represented ORNL, the University of Arizona, Georgia Tech, the University of Tennessee-Knoxville, and Brigham Young University. Symposium attendees represented ORNL, the University of Arizona, Georgia Tech, the University of Tennessee-Knoxville, and Brigham Young University.](/sites/default/files/styles/list_page_thumbnail/public/news/images/2019-P00148%5B2%5D%20r1.jpg?itok=imqhuQWL)
Quantum experts from across government and academia descended on Oak Ridge National Laboratory on Wednesday, January 16 for the lab’s first-ever Quantum Networking Symposium. The symposium’s purpose, said organizer and ORNL senior scientist Nick Peters, was to gather quantum an...
![18-G01703 PinchPoint-v2.jpg 18-G01703 PinchPoint-v2.jpg](/sites/default/files/styles/list_page_thumbnail/public/18-G01703%20PinchPoint-v2.jpg?itok=paJUPDI1)
Researchers used neutron scattering at Oak Ridge National Laboratory’s Spallation Neutron Source to investigate bizarre magnetic behavior, believed to be a possible quantum spin liquid rarely found in a three-dimensional material. QSLs are exotic states of matter where magnetism continues to fluctuate at low temperatures instead of “freezing” into aligned north and south poles as with traditional magnets.
![Joseph Lukens, Raphael Pooser, and Nick Peters (from left) of ORNL’s Quantum Information Science Group developed and tested a new interferometer made from highly nonlinear fiber in pursuit of improved sensitivity at the quantum scale. Credit: Carlos Jones](/sites/default/files/styles/list_page_thumbnail/public/news/images/2018-P09674%5B4%5D.jpg?h=1d98ccbd&itok=ztuyXqpm)
By analyzing a pattern formed by the intersection of two beams of light, researchers can capture elusive details regarding the behavior of mysterious phenomena such as gravitational waves. Creating and precisely measuring these interference patterns would not be possible without instruments called interferometers.