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Media Contacts
![An international team of researchers used Summit to model spin, charge and pair-density waves in cuprates, a type of copper alloy, to explore the materials’ superconducting properties. The results revealed new insights into the relationships between these dynamics as superconductivity develops. Credit: Jason Smith/ORNL](/sites/default/files/styles/list_page_thumbnail/public/2022-02/MaierSpinBanner.png?h=ae114f5c&itok=rdZETb8v)
A study led by researchers at ORNL used the nation’s fastest supercomputer to close in on the answer to a central question of modern physics that could help conduct development of the next generation of energy technologies.
![ORNL’s Marie Kurz examines the many factors affecting the health of streams and watersheds. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-02/2022-P00380--_1.jpg?h=918d9ab1&itok=3Fxfv-4i)
Spanning no less than three disciplines, Marie Kurz’s title — hydrogeochemist — already gives you a sense of the collaborative, interdisciplinary nature of her research at ORNL.
![ORNL’s Brenda Pracheil, left, and Kristine Moody collect water samples at Melton Hill Lake using a sophisticated instrument that collects DNA in the water to determine fish species and number of fish in the water, which could prove useful for monitoring hydropower impacts. Credit: Carlos Jones, ORNL/U.S Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/2020-P17436_0.jpg?h=c6980913&itok=BXPhSslk)
Researchers at Oak Ridge National Laboratory are using a novel approach in determining environmental impacts to aquatic species near hydropower facilities, potentially leading to smarter facility designs that can support electrical grid reliability.
![A new process developed by Oak Ridge National Laboratory leverages deep learning techniques to study cell movements in a simulated environment, guided by simple physics rules similar to video-game play. Credit: MSKCC and UTK](/sites/default/files/styles/list_page_thumbnail/public/2022-01/Observed%20data%20AI%20story%20tip.jpg?h=8e5dac0a&itok=wrAOsfIs)
Scientists have developed a novel approach to computationally infer previously undetected behaviors within complex biological environments by analyzing live, time-lapsed images that show the positioning of embryonic cells in C. elegans, or roundworms. Their published methods could be used to reveal hidden biological activity.
![ORNL scientists used an electron beam for precision machining of nanoscale materials. Cubes were milled to change their shape and could also be removed from an array. Credit: Kevin Roccapriore/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/ORNL_15nm_allmodes_oneper_01.jpg?h=6f770d0b&itok=o5CcrpFN)
Drilling with the beam of an electron microscope, scientists at ORNL precisely machined tiny electrically conductive cubes that can interact with light and organized them in patterned structures that confine and relay light’s electromagnetic signal.
![QLAN submit - A team from the U.S. Department of Energy’s Oak Ridge National Laboratory, Stanford University and Purdue University developed and demonstrated a novel, fully functional quantum local area network, or QLAN, to enable real-time adjustments to information shared with geographically isolated systems at ORNL using entangled photons passing through optical fiber. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/QLAN%20submit_0.jpg?h=cd715a88&itok=JV1MjQHH)
A rapidly emerging consensus in the scientific community predicts the future will be defined by humanity’s ability to exploit the laws of quantum mechanics.
![This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography. Credit: Ada Sedova/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/thermosipho_collabfold2_0.jpg?h=3432ff3c&itok=4xhLbjKZ)
A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.
![ORNL is making underused or inaccessible bioenergy data available to accelerate innovation for the bioeconomy. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/refineryData03_0.jpg?h=bb83cc06&itok=k0NhyfH7)
A research team from Oak Ridge National Laboratory has identified and improved the usability of data that can help accelerate innovation for the growing bioeconomy.