AI @ ORNL – Artificial Intelligence Contact

Oak Ridge National Laboratory has unique expertise and facilities to advance the state of the art in artificial intelligence and apply it to the Department of Energy’s most pressing scientific and national security challenges.

Prasanna Balaprakash, AI Program Director

What's on the horizon

The AI Roadmap
Overview
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Watch: AI at ORNL

Artificial intelligence is no longer just a subject of science fiction. From personal assistants to self-driving cars, AI is revolutionizing everyday life. It’s also revolutionizing scientific discovery. With new algorithms and giant leaps in computing power, AI is accelerating advances in science and technology. ORNL is leading efforts to bring AI into the scientific mainstream.

Three Phases of

Workforce Development

Education

Several pathways ensure that Lab staff continue to lead the world in the application of artificial intelligence for innovation, including ORNL’s summer internship programs and the Bredesen Center Ph.D. Program in Data Science and Engineering.

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Collaboration

ORNL works with vendors, universities, and other national laboratories to offer training opportunities across the artificial intelligence spectrum.

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Users

Lab working groups ensure researchers have the AI knowledge and tools they need to tackle complex challenges across the scientific spectrum.

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Frontier is fully operational
Peak Performance of Frontier (EF)
Staff Using AI
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More to Discover

ORNL's Unique AI Advantages

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ORNL's AI Initiative

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ORNL currently applies artificial intelligence to a wide variety of science and national security applications including materials science, nuclear science, satellite imagery, cyber security, urban dynamics, and health sciences. But the Lab is now looking to harness its resources in theory, experiment, modeling and simulation, and big data to integrate AI across research domains. To this end, ORNL has made a significant internal research investment toward an AI Initiative aimed at advances across the artificial intelligence and analytics spectra. The initiative harnesses the Lab’s unique suite of expertise, compute capabilities, and user facilities to accelerate scientific breakthroughs and enhance national security.

Using various tools developed by ORNL teams, researchers at the lab are adapting artificial intelligence techniques to better understand cancer and other diseases. Through a series of ongoing, interconnected research projects, they are informing health policy and improving public health outcomes. These efforts include a pilot project for the CANcer Distributed Learning Environment (CANDLE) initiative, a joint endeavor led by DOE and the National Cancer Institute that focuses on developing language processing techniques to identify connections between sources of medical data ranging from images to test results to doctors’ notes. Combing through these disparate health records simultaneously would not be feasible with conventional analytical methods.

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AI for Healthcare

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AI for Manufacturing

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At ORNL’s Manufacturing Demonstration Facility, researchers use additive manufacturing, or 3D printing, to develop solid parts made of metal, plastic, and other materials for a wide range of applications. By incorporating AI into this process, they aim to reliably produce more energy-efficient, durable, and customizable products at a lower cost. These high-quality designs could lead to longer-lasting vehicles, faster personal computers, better insulation in houses, and even more specialized aerospace components. Using artificial intelligence image processing techniques developed at ORNL, researchers can inspect parts in real time throughout the manufacturing process to locate cracks and other quality defects, preventing problems further into the manufacturing process and, by extension, reducing costs and time to market.

AI, in combination with the Summit supercomputer and other resources at the lab, accelerates research across the broad spectrum of energy generation, distribution, storage, and security. For example, scientists use AI to study clean energy sources and mitigate environmental impacts by running climate simulations, developing new materials, and creating carbon-capture technologies. Additionally, AI helps ORNL staff improve certain maintenance and operation practices, extending the lifetimes and energy production capabilities of nuclear power plants and other facilities. AI techniques can also be used in studies related to the nation’s power grid, ensuring both cyber and physical security during the delivery of electricity to homes, businesses, and public spaces as part of DOE’s Grid Modernization Initiative.

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Ai for Energy

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AI for Security

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Cybersecurity experts are constantly collecting data, but sifting through information to pinpoint problem areas in a timely manner can be difficult. To address this challenge, new systems like ORNL’s “Situ” platform monitor thousands of events per second to detect anomalies that would escape human analysts. After sorting through massive amounts of raw network activity, Situ provides network operators with smaller amounts of data that are easier to manage and review, allowing them to investigate potential threats and make more informed decisions. Through partnerships with Chattanooga’s Electric Power Board and other companies, ORNL also helps maintain energy security by deploying AI-enabled security systems capable of monitoring data streams and identifying suspicious activity.

ORNL’s Frontier is more than the world's first exascale machine—it’s also smart. This HPE Cray EX system opened to users in 2023 and resides at the Oak Ridge Leadership Computing Facility, a DOE Office of Science User Facility located at ORNL. With computing power and architecture components optimized for AI applications, Frontier helps researchers apply machine learning and deep learning techniques to a variety of science problems to quickly obtain accurate results. Frontier's increased memory bandwidth means researchers can tackle data-intensive problems without the need to constantly move data. As a result, unimpeded deep learning algorithms run at faster speeds and reach higher levels of accuracy than would be possible on another system.

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AI for Supercomputing

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