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Media Contacts
![Frontier supercomputer](/sites/default/files/styles/list_page_thumbnail/public/2023-06/Frontier-logos_0.jpg?h=c6980913&itok=yuF5A0wj)
Innovations in artificial intelligence are rapidly shaping our world, from virtual assistants and chatbots to self-driving cars and automated manufacturing.
![ORNL seismic researcher Chengping Chai placed seismic sensors on the ground at various distances from an ORNL nuclear reactor to learn whether they could detect its operating state. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-06/2023-P03398.jpg?h=3e43625b&itok=TXK8tthh)
Like most scientists, Chengping Chai is not content with the surface of things: He wants to probe beyond to learn what’s really going on. But in his case, he is literally building a map of the world beneath, using seismic and acoustic data that reveal when and where the earth moves.
For the third year in a row, the Quantum Science Center held its signature workforce development event: a comprehensive summer school for students and early-career scientists designed to facilitate conversations and hands-on activities related to
![Exascale Computing Project](/sites/default/files/styles/list_page_thumbnail/public/2023-05/ECP.png?h=f9180d14&itok=jDHXXWP2)
Lawrence Livermore National Laboratory’s Lori Diachin will take over as director of the Department of Energy’s Exascale Computing Project on June 1, guiding the successful, multi-institutional high-performance computing effort through its final stages.
![A study led by ORNL researchers examines the causes behind ordering of cations, the positive ions that help make double perovskite oxides look promising as an energy source. Credit: Getty Images](/sites/default/files/styles/list_page_thumbnail/public/2023-05/CationBanner.png?h=ae114f5c&itok=czF5YUhD)
A study led by researchers at ORNL could uncover new ways to produce more powerful, longer-lasting batteries and memory devices.
![Ashley Barker. Credit: Carlos Jones/ORNL](/sites/default/files/styles/list_page_thumbnail/public/2023-05/2022-P14022.jpg?h=c6980913&itok=Su1w1TDa)
At the National Center for Computational Sciences, Ashley Barker enjoys one of the least complicated–sounding job titles at ORNL: section head of operations. But within that seemingly ordinary designation lurks a multitude of demanding roles as she oversees the complete user experience for NCCS computer systems.
![ORNL researchers encoded grid hardware operating data into a color band hidden inside photographs, video or artwork, as shown in this photo. The visual can then be transmitted to a utility’s control center for decoding. Credit: ORNL/U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-05/Color%20bar.ORNL_.jpg?h=a49d782d&itok=eX3P5GZU)
Inspired by one of the mysteries of human perception, an ORNL researcher invented a new way to hide sensitive electric grid information from cyberattack: within a constantly changing color palette.
![This image depicts a visualization of an outflow of galactic wind at a single point in time using Cholla. Credit: Evan Schneider/University of Pittsburgh](/sites/default/files/styles/list_page_thumbnail/public/2023-04/cholla_image001.png?h=e7fd8fff&itok=Jj11Uvtl)
A trio of new and improved cosmological simulation codes was unveiled in a series of presentations at the annual April Meeting of the American Physical Society in Minneapolis.
![An Oak Ridge National Laboratory study compared classical computing techniques for compressing data with potential quantum compression techniques. Credit: Getty Images](/sites/default/files/styles/list_page_thumbnail/public/2023-04/QuantumCompression.png?h=9fa9abd8&itok=o0n1r7et)
A study led by Oak Ridge National Laboratory researchers identifies a new potential application in quantum computing that could be part of the next computational revolution.
![An AI-generated image representing atoms and artificial neural networks. Credit: Maxim Ziatdinov, ORNL](/sites/default/files/styles/list_page_thumbnail/public/2023-04/atoms3.jpg?h=ab622562&itok=dNMzrFw8)
Researchers at ORNL have developed a machine-learning inspired software package that provides end-to-end image analysis of electron and scanning probe microscopy images.