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In coordination with six other national labs, Fermilab is developing AI tools to increase the efficiency and innovation in particle accelerators as part of the Department of Energy’s Genesis Mission.
Particle accelerators represent some of humanity's most powerful tools, driving significant discoveries across physics, chemistry, materials science, and biology. They are integral to modern advancements, from producing medical isotopes for cancer treatment to supporting fusion research and developing methods to eliminate 'forever chemicals' in water. Despite their immense utility, these machines are incredibly complex, demanding years for research, design, and construction. Advanced particle accelerators involve tens to hundreds of thousands of devices that must operate in perfect synchronicity to deliver a wide variety of particle beams, presenting substantial challenges in both development and ongoing operation.
To address the inherent complexities and operational challenges of particle accelerators, Fermilab is playing a central role in the Multi-Office particle Accelerator Team, known as MOAT. This initiative is focused on creating a unified, advanced artificial intelligence system designed for seamless integration across the entire lifecycle of particle accelerators. The overarching vision for MOAT, as articulated by Jonathan Jarvis, director of Fermilab’s Accelerator Research Division, is to embed AI so completely into the design, construction, and operational processes that it fundamentally transforms the pace of scientific discovery and the resulting innovations.
MOAT is a crucial component of the U.S. Department of Energy’s Genesis Mission, a historic and ambitious undertaking aimed at advancing AI to accelerate scientific discovery. Specifically, MOAT operates as part of the Transformational AI Models Consortium (ModCon), which is tasked with developing and deploying self-improving AI models. This endeavor leverages the extensive data resources, specialized facilities, and deep expertise of the DOE. The collaborative effort to develop MOAT includes researchers from seven prominent DOE national laboratories: Lawrence Berkeley, Argonne, Fermilab, Jefferson, Oak Ridge, SLAC, and Brookhaven, highlighting the broad impact of accelerators across diverse scientific fields.
Fermilab’s cutting-edge accelerator technology test facility, FAST/IOTA, has been designated as a key demonstrator and testbed for MOAT's developing AI tools. This facility offers unique flexibility for evaluating various types of accelerators and particle beams. MOAT recently presented its initial progress to the DOE Office of Science, showcasing the first deployment of its Osprey AI tool. Osprey employs advanced AI agents, which are autonomous software systems capable of reasoning, planning, and executing tasks with minimal human supervision. This tool has demonstrated the ability to accelerate specific tasks by a factor of 100, signifying a major leap forward in collaborative AI software development within the scientific community.
A core objective for MOAT's AI systems is to optimize the operations of existing particle accelerators. This involves harnessing decades of accumulated operational knowledge from various complexes, including Fermilab's. AI models will be trained on documented problem-solving instances, providing immediate and referenced solutions for operational issues. Additionally, MOAT plans to develop sophisticated 'digital twins' for each accelerator complex. These virtual replicas will be dynamically interconnected with their real-world counterparts, establishing continuous feedback loops. This capability will enable virtual diagnostics and speculative beam tuning in a simulated environment before any physical adjustments are made, allowing the AI to learn and enhance the accuracy of the digital twin. This approach is projected to save billions of dollars, reduce development timelines by years, and dramatically boost accelerator performance and scientific output.