Scientists at ORNL used neutron scattering to determine whether a specific material’s atomic structure could host a novel state of matter called a spiral spin liquid.
Oak Ridge National Laboratory’s Innovation Crossroads program welcomes six new science and technology innovators from across the United States to the sixth cohort.
Burak Ozpineci, a Corporate Fellow and section head for Vehicle and Mobility Systems Research at Oak Ridge National Laboratory, is one of six international recipients of the eighth Nagamori Award.
To solve a long-standing puzzle about how long a neutron can “live” outside an atomic nucleus, physicists entertained a wild but testable theory positing the existence of a right-handed version of our left-handed universe.
Oak Ridge National Laboratory researchers developed an invertible neural network, a type of artificial intelligence that mimics the human brain, to improve accuracy in climate-change models and predictions.
The Frontier supercomputer at the Department of Energy’s Oak Ridge National Laboratory earned the top ranking today as the world’s fastest on the 59th TOP500 list, with 1.1 exaflops of performance. The system is the first to achieve an unprecedented level of computing performance known as exascale, a threshold of a quintillion calculations per second.
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.
Researchers at ORNL are teaching microscopes to drive discoveries with an intuitive algorithm, developed at the lab’s Center for Nanophase Materials Sciences, that could guide breakthroughs in new materials for energy technologies, sensing and computing.
Oak Ridge National Laboratory researchers determined that for every 5 miles per hour that drivers travel over a 50-mph speed limit, fuel economy decreases by 7% and equates to paying an extra 28 cents per gallon at current.
An Oak Ridge National Laboratory team developed a novel technique using sensors to monitor seismic and acoustic activity and machine learning to differentiate operational activities at facilities from “noise” in the recorded data.