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NCCS Director Arjun Shankar gives an update on the facility’s next high-performance computing system during the OLCF User Meeting on Sept. 10, 2024.   Credit: Kurt Weiss/ORNL, U.S. Dept. of Energy

The Oak Ridge Leadership Computing Facility welcomed users to an interactive meeting at the Department of Energy’s Oak Ridge National Laboratory from Sept. 10–11 for an opportunity to share achievements from the OLCF’s user programs and highlight requirements for the future.

The Frontier supercomputer simulated magnetic responses inside calcium-48, depicted by red and blue spheres. Insights into the nucleus’s fundamental forces could shed light on supernova dynamics.

Nuclear physicists at the Department of Energy’s Oak Ridge National Laboratory recently used Frontier, the world’s most powerful supercomputer, to calculate the magnetic properties of calcium-48’s atomic nucleus. 

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.

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.

Dmytro Bykov, left, and Hector Corzo participate in a value proposition development exercise as part Energy I-Corps

Two ORNL teams recently completed Cohort 18 of Energy I-Corps, an immersive two-month training program where the scientists define their technology’s value propositions, conduct stakeholder discovery interviews and develop viable market pathways.

This photo is of a male scientist sitting at a desk working with materials, wearing protective glasses.

Researchers at the Department of Energy’s Oak Ridge National Laboratory and partner institutions have launched a project to develop an innovative suite of tools that will employ machine learning algorithms for more effective cybersecurity analysis of the U.S. power grid. 

Image is of woman with dark hair crossing her arms and posting for a photo in front of a blue backdrop. She is wearing a black shirt with a tan cardigan.

Researcher Rocio Uria-Martinez was named one of four “Women with Hydro Vision” at this year’s HYDROVISION International 2024 conference taking place in Denver this week. Awarded by a committee of industry peers, the honor recognizes women who use their unique talents and vision to improve and advance the worldwide hydropower industry. 

Three team members looking at plants stand in front of a mountain scene, two are in orange safety vests.

When Oak Ridge National Laboratory's science mission takes staff off-campus, the lab’s safety principles follow. That’s true even in the high mountain passes of Washington and Oregon, where ORNL scientists are tracking a tree species — and where wildfires have become more frequent and widespread.

Man in blue shirt and grey pants holds laptop and poses next to a green plant in a lab.

John Lagergren, a staff scientist in Oak Ridge National Laboratory’s Plant Systems Biology group, is using his expertise in applied math and machine learning to develop neural networks to quickly analyze the vast amounts of data on plant traits amassed at ORNL’s Advanced Plant Phenotyping Laboratory.

A team led by Oak Ridge National Laboratory researchers used Frontier to explore training strategies for one of the largest artificial intelligence models to date. Credit: Getty Images

A team led by researchers at ORNL explored training strategies for one of the largest artificial intelligence models to date with help from the world’s fastest supercomputer. The findings could help guide training for a new generation of AI models for scientific research.