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Director of ORNL’s AI Initiative Prasanna Balaprakash addresses attendees at the Generative AI for ORNL Science Workshop. Credit: ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory hosted its Smoky Mountains Computational Science and Engineering Conference for the first time in person since the COVID pandemic broke in 2020. The conference, which celebrated its 20th consecutive year, took place at the Crowne Plaza Hotel in downtown Knoxville, Tenn., in late August.

Oak Ridge National Laboratory entrance sign

The Department of Energy’s Office of Science has selected three ORNL research teams to receive funding through DOE’s new Biopreparedness Research Virtual Environment initiative.

ORNL researchers are demonstrating an automation system for this portable system, currently based in Colorado, for treatment of non-traditional water sources to drinking water standards. Credit: Tzahi Cath/Colorado School of Mines

Researchers at ORNL are developing advanced automation techniques for desalination and water treatment plants, enabling them to save energy while providing affordable drinking water to small, parched communities without high-quality water supplies.

The DEMAND single crystal diffractometer at the High Flux Isotope Reactor, or HFIR, is the latest neutron instrument at the Department of Energy’s Oak Ridge National Laboratory to be equipped with machine learning-assisted software, called ReTIA. Credit: Jeremy Rumsey/ORNL, U.S. Dept. of Energy

Neutron experiments can take days to complete, requiring researchers to work long shifts to monitor progress and make necessary adjustments. But thanks to advances in artificial intelligence and machine learning, experiments can now be done remotely and in half the time.

ZEISS Head of Additive Manufacturing Technology Claus Hermannstaedter, left, and ORNL Interim Associate Laboratory Director for Energy Science and Technology Rick Raines sign a licensing agreement that allows ORNL’s machine-learning algorithm, Simurgh, to be used for rapid evaluations of 3D-printed components with industrial X-ray computed tomography, or CT. Using machine learning in CT scanning is expected to reduce the time and cost of inspections of 3D-printed parts by more than ten times.

A licensing agreement between the Department of Energy’s Oak Ridge National Laboratory and research partner ZEISS will enable industrial X-ray computed tomography, or CT, to perform rapid evaluations of 3D-printed components using ORNL’s machine

Group of young kids sitting at a lab table.

A group at the Department of Energy's Oak Ridge National Laboratory made a difference for local youth through hands-on projects that connected neutron science and engineering intuitively.

Cody Lloyd stands in front of images of historical nuclear field testing. The green and red dots are the machine learning algorithm recognizing features in the image. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Cody Lloyd became a nuclear engineer because of his interest in the Manhattan Project, the United States’ mission to advance nuclear science to end World War II. As a research associate in nuclear forensics at ORNL, Lloyd now teaches computers to interpret data from imagery of nuclear weapons tests from the 1950s and early 1960s, bringing his childhood fascination into his career

Neutron experiments helped reveal the one-carbon enzymatic mechanism that synthesizes vital food sources for cancer cells that depend on vitamin B6, providing key insights into designing novel drugs to slow the spread of aggressive cancers. Credit: Jill Hemman/ORNL, U.S. Dept. of Energy

After a highly lauded research campaign that successfully redesigned a hepatitis C drug into one of the leading drug treatments for COVID-19, scientists at ORNL are now turning their drug design approach toward cancer. 

Credit: NAIC Arecibo Observatory, a facility of the NSF; (INSET) Michelle Negron, National Science Foundation

For more than half a century, the 1,000-foot-diameter spherical reflector dish at the Arecibo Observatory in Puerto Rico was the largest radio telescope in the world. Completed in 1963, the dish was built in a natural sinkhole, with the telescope’s feed antenna suspended 500 feet above the dish on a 1.8-million-pound steel platform. Three concrete towers and more than 4 miles of steel cables supported the platform.

Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

After completing a bachelor’s degree in biology, Toya Beiswenger didn’t intend to go into forensics. But almost two decades later, the nuclear security scientist at ORNL has found a way to appreciate the art of nuclear forensics.