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Frontier supercomputer cabinets

Experts at the Department of Energy’s national laboratories are working with university and industry partners to improve machine learning and deep learning by reducing costs and times to solution. And as AI systems become increasingly integral to critical decision-making processes and more deeply integrated into research workflows, ensuring their reliability and accuracy is more important than ever.

oxygen isotope 28

Rare isotope oxygen-28 has been determined to be "barely unbound" by experiments led by researchers at the Tokyo Institute of Technology and by computer simulations conducted at ORNL. The findings from this first-ever observation of 28O answer a longstanding question in nuclear physics: can you get bound isotopes in a very neutron-rich region of the nuclear chart, where instability and radioactivity are the norm? 

Madhavi Martin portrait image

Madhavi Martin brings a physicist’s tools and perspective to biological and environmental research at the Department of Energy’s Oak Ridge National Laboratory, supporting advances in bioenergy, soil carbon storage and environmental monitoring, and even helping solve a murder mystery.

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

Mike Huettel

Mike Huettel is a cyber technical professional. He also recently completed the 6-month Cyber Warfare Technician course for the United States Army, where he learned technical and tactical proficiency leadership in operations throughout the cyber domain.

Rose Montgomery

Rose Montgomery, a distinguished researcher and leader of the Used Fuel and Nuclear Material Disposition group at ORNL, has been selected to participate in the U.S. WIN Nuclear Executives of Tomorrow, or NEXT, class of 2023 to 2024.

Autonomous additive manufacturing, or AI- guided 3D printing, can accurately estimate the strength and quality of printed components. This system collects information about conditions during production, including temperature, that influence the properties and quality of printed objects. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Autonomous labs are changing the nature of scientific investigation. Instead of humans manually orchestrating every part of an experiment, programmed equipment can carry out necessary functions. This workflow accelerates the pace of discovery by reducing the number of monotonous tasks that researchers must perform.

The OpeN-AM experimental platform, installed at the VULCAN instrument, features a robotic arm that prints layers of molten metal to create complex shapes. Credit: Jill Hemman/ORNL, U.S Dept. of Energy

Technologies developed by researchers at ORNL have received six 2023 R&D 100 Awards.  

Diagram of faults affecting a conventional power system.

Researchers at the Department of Energy’s Oak Ridge National Laboratory are leading the way in understanding the effects of electrical faults in the modern U.S. power grid.

From left, Gladisol Smith Vega prepares to collect field data on the Oak Ridge Reservation with mentor Scott Brooks. Credit: Carlos Jones/ORNL. U.S. Dept. of Energy

Nearly 100 interns were introduced to Oak Ridge National Laboratory’s biological and environmental research over the summer of 2023 as mentors and students were eager to share knowledge and skills to address the nation’s energy and environmental challenges.