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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.

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.

Bryan Maldonado

As a mechanical engineer in building envelope materials research at ORNL, Bryan Maldonado sees opportunities to apply his scientific expertise virtually everywhere he goes, from coast to coast. As an expert in understanding how complex systems operate, he’s using machine learning methods to control the process and ultimately optimize performance. 

Ayana Ghosh

A research scientist with the Department of Energy’s Oak Ridge National Laboratory, Ayana Ghosh was named the 2024 Early Discovery Award winner by the American Ceramic Society. The award recognizes an early career member of the organization who has contributed to basic science in the field of glass and ceramics.