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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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HempWool, Hempitecture’s hemp fiber batt insulation is being cut here

Hempitecture, a graduate of the Innovation Crossroads program, has been awarded $8.4 million by the DOE's Office of Manufacturing and Energy Supply Chains. As part of the grant, Hempitecture will establish a facility in East Tennessee. 

10 members of JUMP into STEM leadership are standing on the ORNL quad posting with the poster

Science, technology, engineering and math students from colleges across the nation who participate in the next DOE Building Technologies Office’s JUMP into STEM competition will tackle three new challenges: building affordability, peak power demand and indoor comfort in extreme climates. 

Shantonio Birch, founder of ThermoVerse is pictured here

ThermoVerse and Expanding Frontiers have made strides in energy innovation by securing wins in Phase 2 of the Department of Energy’s American-Made Lab MATCH Prize, a competition designed to accelerate commercialization of national laboratory technologies.

A graphic representation of AI

The Department of Energy announced a $67 million investment in several AI projects from institutions in both government and academia as part of its AI for Science initiative. Six ORNL-led (or co-led) projects received funding.

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. 

ORNL’s Prasanna Balaprakash joined leading computing experts to provide insight into how supercomputing, AI and meteorology can work together to advance weather and climate research as part of a panel for the United States Senate.

Prasanna Balprakash, director of AI programs for ORNL, discussed advancing climate and weather research through high performance computing and artificial intelligence as part of a September 18 panel for the United States Senate. 

155 attendees from all over the world gathered for SMC24 for a wide range of presentations from industry leading experts.

The Smoky Mountain Computational Sciences and Engineering Conference, or SMC24, entered its third decade with the 21st annual gathering in East Tennessee.

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 Oak Ridge National Laboratory 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.

pulsed laser deposition setup

In a game-changing study, ORNL scientists developed a deep learning model — a type of artificial intelligence that mimics human brain function — to analyze high-speed videos of plasma plumes during a process called pulsed laser deposition.