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Larry York is sitting in front of a computer screen showing an image of plant phenotyping

The Advanced Plant Phenotyping Laboratory at ORNL utilizes robotics, multi-modal imaging, and AI to enhance understanding of plant genetics and interactions with microbes. It aims to connect genes to traits for advancements in bioenergy, agriculture, and climate resilience. Senior scientist Larry York highlights the lab's capabilities and the insights from a new digital underground imaging system to improve biomass feedstocks for bioenergy and carbon storage.

This is a simulated image of the project to build a new network that artificial intelligence and machine learning to steer experiments and analyze data faster and more accurately. will enable

To bridge the gap between experimental facilities and supercomputers, experts from SLAC National Accelerator Laboratory are teaming up with other DOE national laboratories to build a new data streaming pipeline. The pipeline will allow researchers to send their data to the nation’s leading computing centers for analysis in real time even as their experiments are taking place. 

Team working on in green composites design for their fully-recyclable wind turbine blade tip incorporating low-cost carbon fiber

ORNL researchers were honored with a prestigious ACE Award for Composites Excellence by the American Composites Manufacturers Association. The team won the “innovation in green composites design” prize for creating a fully recyclable, lightweight wind turbine blade tip that incorporates low-cost carbon fiber and conductive coating for enhanced protection against lightning strikes. 

MedUSE wire-arc

Researchers at ORNL are using advanced manufacturing techniques to revitalize the domestic production of very large metal parts that weigh at least 10,000 pounds each and are necessary for a variety of industries, including energy.

West named SME 30 Under 30 honoree for 2024

Justin West, an advanced machining and machine tool researcher at ORNL, has been selected as a recipient of the 2024 30 Under 30 award by the Society of Manufacturing Engineers.

This illustration demonstrates how atomic configurations with an equiatomic concentration of niobium (Nb), tantalum (Ta) and vanadium (V) can become disordered. The AI model helps researchers identify potential atomic configurations that can be used as shielding for housing fusion applications in a nuclear reactor. Credit: Massimiliano Lupo Pasini/ORNL, U.S. Dept. of Energy

A study led by the Department of Energy’s 91°µÍř details how artificial intelligence researchers created an AI model to help identify new alloys used as shielding for housing fusion applications components in a nuclear reactor. The findings mark a major step towards improving nuclear fusion facilities.

ORNL’s convergent manufacturing platform, on display at IMTS 2024 in Chicago, Illinois, integrates multiple systems into one.

A new convergent manufacturing platform, developed in only five months at the Department of Energy’s 91°µÍř, is debuting at the International Manufacturing Technology Show, or IMTS, in Chicago, Sept. 9–12, 2024.

From left, Sedrick Bouknight and Matthias Maiterth of ORNL’s Analytics and AI Methods at Scale group demonstrate the VR capabilities of the Frontier digital twin project's ExaDIGIT framework. Using VR allows Frontier's operators to exam the system's telemetry in a more interactive and intuitive way.

As high-tech companies ramp up construction of massive data centers to meet the business boom in artificial intelligence, one component is becoming an increasingly rare commodity: electricity. Since its formation in 2004, the OLCF has fielded five generations of world-class supercomputing systems that have produced a nearly 2,000 times reduction in energy usage per floating point operation per second, or flops. With decades of experience in making HPC more energy efficient, the OLCF may serve as a resource for best “bang for the buck” practices in a suddenly burgeoning industry.

ORNL scientists used molecular dynamics simulations, exascale computing, lab testing and analysis to accelerate the development of an energy-saving method to produce nanocellulosic fibers.

A team led by scientists at ORNL identified and demonstrated a method to process a plant-based material called nanocellulose that reduced energy needs by a whopping 21%, using simulations on the lab’s supercomputers and follow-on analysis.

Wavy photo representing high performance computing

Office of Science to announce a new research and development opportunity led by ORNL to advance technologies and drive new capabilities for future supercomputers. This industry research program worth $23 million, called New Frontiers, will initiate partnerships with multiple companies to accelerate the R&D of critical technologies with renewed emphasis on energy efficiency for the next generation of post-exascale computing in the 2029 and beyond time frame.