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Media Contacts
A multi-lab research team led by ORNL's Paul Kent is developing a computer application called QMCPACK to enable precise and reliable predictions of the fundamental properties of materials critical in energy research.
Researchers from ORNL, the University of Tennessee at Chattanooga and Tuskegee University used mathematics to predict which areas of the SARS-CoV-2 spike protein are most likely to mutate.
Oak Ridge National Laboratory researchers are developing a first-of-its-kind artificial intelligence device for neutron scattering called Hyperspectral Computed Tomography, or HyperCT.
Researchers at Oak Ridge National Laboratory and Momentum Technologies have piloted an industrial-scale process for recycling valuable materials in the millions of tons of e-waste generated annually in the United States.
Researchers at Oak Ridge National Laboratory are using state-of-the-art methods to shed light on chemical separations needed to recover rare-earth elements and secure critical materials for clean energy technologies.
New polymer materials under development at Oak Ridge National Laboratory could enable safer, more stable batteries needed for electric vehicles and grid energy storage.
Researchers at Oak Ridge National Laboratory and Korea’s Sungkyunkwan University are using advanced microscopy to nanoengineer promising materials for computing and electronics in a beyond-Moore era.
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.
Scientists are using Oak Ridge National Laboratory’s Multicharged Ion Research Facility to simulate the cosmic origin of X-ray emissions resulting when highly charged ions collide with neutral atoms and molecules, such as helium and gaseous hydrogen.
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.