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Hood Whitson, chief executive officer of Element3, and Cynthia Jenks, associate laboratory director for the Physical Sciences Directorate, shake hands during the Element3 licensing event at ORNL on May 3, 2024. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

A collection of seven technologies for lithium recovery developed by scientists from ORNL has been licensed to Element3, a Texas-based company focused on extracting lithium from wastewater produced by oil and gas production. 

ORNL researchers are establishing a digital thread of data, algorithms and workflows to produce a continuously updated model of earth systems.

Digital twins are exactly what they sound like: virtual models of physical reality that continuously update to reflect changes in the real world.

 

Frontier, the fastest supercomputer in the world, provides expansive and energy-efficient power, which gives scientists the capability to train large AI models in a responsible way.

ORNL is home to the world's fastest exascale supercomputer, Frontier, which was built in part to facilitate energy-efficient and scalable AI-based algorithms and simulations. 

Ramesh Bhave in lab

Caldera Holding, the owner and developer of Missouri’s Pea Ridge iron mine, has entered a nonexclusive research and development licensing agreement with ORNL to apply a membrane solvent extraction technique, or MSX, developed by ORNL researchers to mined ores.

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In response to a renewed international interest in molten salt reactors, researchers from the Department of Energy’s Oak Ridge National Laboratory have developed a novel technique to visualize molten salt intrusion in graphite.

The Department of Energy’s Oak Ridge National Laboratory announced the establishment of its Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making. Credit: Rachel Green/ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory announced the establishment of the Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making.

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.

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.

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

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