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ORNL's Communications team works with news media seeking information about the laboratory. Media may use the resources listed below or send questions to news@ornl.gov.

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Paul Kairys

Paul is exploring the next frontier: bridging quantum computing with neutron science. His research aims to integrate quantum algorithms with neutron scattering experiments, opening new possibilities for understanding materials at an atomic level.

ORNL's Quantum Science Center Director is speaking to a attendee at Purdue University Quantum Science Center Summer School poster presentation

The fifth annual Quantum Science Center, or QSC, Summer School at Purdue University, held Apr. 21 through Apr. 25, 2025, welcomed its largest group of students to date. Experts from industry, academia and national laboratories gathered at the Purdue Quantum Science and Engineering Institute to share their research in multiple areas of quantum science.

Oak Ridge High School student is working on an 3D printing machine donated by UT-Battelle

UT-Battelle has contributed up to $475,000 for the purchase and installation of advanced manufacturing equipment to support a program at Tennessee’s Oak Ridge High School that gives students direct experience with the AI- and robotics-assisted workplace of the future. 

The heartbeat Detector is pictured here, which is a black rectangular box with a heartbeat line and wording on the top to reflect its name

The Heartbeat Detector, developed at ORNL and licensed by Geovox Security Inc., detects hidden individuals in vehicles by measuring suspension vibrations. Now using a compact black box and cloud software, the system is more affordable and easier to use, while remaining the industry standard worldwide.

Two ORNL researchers are standing to the right of a computer screen and a poster promoting the AI Expo
The Department of Energy’s Oak Ridge National Laboratory gathered more than 200 artificial intelligence experts and domain scientists for an AI expo exploring cutting edge artificial intelligence that’s making a difference for scientific research
A 3D printing nozzle wrapped in insulation extrudes black composite material into a small square mold on a green and white flat surface in a lab setting. Inset shows a close-up of a pressure gauge connected to brass valves and tubing.

Scientists at ORNL have developed a vacuum-assisted extrusion method that reduces internal porosity by up to 75% in large-scale 3D-printed polymer parts. This new technique addresses the critical issue of porosity in large-scale prints but also paves the way for stronger composites. 

Image of the Frontier supercomputer in black with Frontier spelled out across the cabinets in front.

Research teams at the Department of Energy’s Oak Ridge National Laboratory received computing resource awards to train and test AI foundation models for science. A total of six ORNL projects were awarded allocations from the National Artificial Intelligence Research Resource, or NAIRR, pilot and the Innovative and Novel Computational Impact on Theory and Experiment, or INCITE, program to train their AI models.

ORNL researcher Jesse Labbe is working with plants in a greenhouse. He is framed on all sides with bright green leaves

Jesse Labbé aims to leverage biology, computation and engineering to address societal challenges related to energy, national security and health, while enhancing U.S. competitiveness. Labbé emphasizes the importance of translating groundbreaking research into practical applications that have real-world impact.

Group of 11 people, 9 standing and two sitting are posing for a photo in front of University of Oklahoma red and white backdrop with UO logo. The two in front are shaking hands

The University of Oklahoma and Oak Ridge National Laboratory, the Department of Energy’s largest multi-program science and energy laboratory, have entered a strategic collaboration to establish a cutting-edge additive manufacturing center. 

Illustration of a glowing black box emitting digital particles that form into a 3D model of an electrical grid infrastructure, set against a background of binary code and data visualizations.

Researchers at Oak Ridge National Laboratory have developed a modeling method that uses machine learning to accurately simulate electric grid behavior while protecting proprietary equipment details. The approach overcomes a key barrier to accurate grid modeling, helping utilities plan for future demand and prevent blackouts.