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ORNL researchers Tom Beck, left, Sarp Oral and Rafael Ferreira da Silva have proposed a strategy for integrating classical supercomputers such as Frontier, the world’s first exascale computer, with the emerging field of quantum computing.

A study by more than a dozen scientists at the Department of Energy’s Oak Ridge National Laboratory examines potential strategies to integrate quantum computing with the world’s most powerful supercomputing systems in the pursuit of science.

Jeremiah Sewell

Jeremiah Sewell leads a team at ORNL, working on xenon-129 production for lung imaging. Reflecting on his career, Sewell views each opportunity as a "door" he steps through, leveraging over 25 years of experience in nuclear power and centrifuge operations to advance the facility’s mission.

Debjani Singh

Debjani Singh, a senior scientist at ORNL, leads the HydroSource project, which enhances hydropower research by making water data more accessible and useful. With a background in water resources, data science, and earth science, Singh applies innovative tools like AI to advance research. Her career, shaped by her early exposure to science in India, focuses on bridging research with practical applications.

Weyl semimetal

At ORNL, a group of scientists used neutron scattering techniques to investigate a relatively new functional material called a Weyl semimetal. These Weyl fermions move very quickly in a material and can carry electrical charge at room temperature. Scientists think that Weyl semimetals, if used in future electronics, could allow electricity to flow more efficiently and enable more energy-efficient computers and other electronic devices.

Image with a grey and black backdrop - in front is a diamond with two circles coming out from it, showing the insides.

The world’s fastest supercomputer helped researchers simulate synthesizing a material harder and tougher than a diamond — or any other substance on Earth. The study used Frontier to predict the likeliest strategy to synthesize such a material, thought to exist so far only within the interiors of giant exoplanets, or planets beyond our solar system.

Green and blue background of a graphic image that says Honors and Awards

Two additive manufacturing researchers from ORNL received prestigious awards from national organizations. Amy Elliott and Nadim Hmeidat, who both work in the Manufacturing Science Division, were recognized recently for their early career accomplishments.

Man is leaning against the window, arms crossed in a dark navy button up.

Brian Sanders is focused on impactful, multidisciplinary science at Oak Ridge National Laboratory, developing solutions for everything from improved imaging of plant-microbe interactions that influence ecosystem health to advancing new treatments for cancer and viral infections. 

Researcher Brittany Rodriguez works with an ORNL-developed Additive Manufacturing/Compression Molding system that 3D prints large-scale, high-volume parts made from lightweight composites. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Brittany Rodriguez never imagined she would pursue a science career at a Department of Energy national laboratory. However, after some encouraging words from her mother, input from key mentors at the University of Texas Rio Grande Valley, or UTRGV, and a lot of hard work, Rodriguez landed at DOE’s Manufacturing Demonstration Facility, or MDF, at Oak Ridge National Laboratory.

Oak Ridge National Laboratory building and sign for the Computing and Computational Sciences Directorate.

The contract will be awarded to develop the newest high-performance computing system at the Oak Ridge Leadership Computing Facility.

The photo is of a 3D-printed part -- a big grey block with a grey fan like structure coming out from the top. To the right shows a digital copy in an AI model.

The Department of Energy’s Oak Ridge National Laboratory has publicly released a new set of additive manufacturing data that industry and researchers can use to evaluate and improve the quality of 3D-printed components. The breadth of the datasets can significantly boost efforts to verify the quality of additively manufactured parts using only information gathered during printing, without requiring expensive and time-consuming post-production analysis.