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DOE selects Fermilab-led AI initiative to advance particle accelerator performance. August 20, 2026 Press play to listen to this content Aug. 20, 2026 - In a project selected for funding by the Department of Energy's Genesis Mission, Fermi National Accelerator Laboratory and partners from other national laboratories, universities and industry will apply artificial intelligence and machine learning to enable precise and adaptive resonance control in particle accelerators. Their work aims to lower operating costs by millions of dollars each year and improve accelerator performance, driving scientific discovery. Superconducting radio-frequency cavities are being assembled and tested for installation at the Proton Improvement Plan-II, part of the Fermilab Accelerator Complex. By fine tuning the cavities' resonance, scientists can optimize accelerator performance. Credit: Ryan Postel, Fermilab Inside the particle accelerators that power modern high-energy physics research, timing is everything. A fraction of a second off, and the delicate rhythm that keeps subatomic particles racing through an accelerator can slip out of tune. Now a Fermilab-led team will leverage artificial intelligence to protect that rhythm. Their project, selected for the Department of Energy's Genesis Mission, is poised to reshape how accelerators are controlled, optimized and operated. The most powerful modern particle accelerators use superconducting radio-frequency, or SRF, cavities to transfer energy to particle beams. By creating strong electromagnetic fields and leveraging a phenomenon called resonance, they give gentle nudges at exactly the right times to charged subatomic particles - much like a playground swing given repeated, well-timed pushes to send it higher. As the effects of these nudges accumulate, the particles in an accelerator are pushed faster and faster - close to the speed of light - until they collide with other particles traveling in the opposite direction or with a fixed target. The data from these collisions enables scientists to investigate and understand the underlying principles governing our universe. While SRF cavities are extremely efficient resonators, several factors can stand in the way of perfect performance. They can be knocked off frequency by pressure variations in the liquid helium that cools them, changes in their electromagnetic fields, or vibrations from other nearby equipment. These disturbances interfere with resonance, wasting power and potentially tripping off the particle beam. Precise resonance control increases available power, resulting in higher particle beam performance and new discoveries. "Controlling resonance is a critical area of development for particle accelerator facilities, potentially saving millions of dollars a year on operating costs, optimizing power consumption and improving beam stability for accurate scientific results and increasing equipment lifetimes," said Matthias Liepe, a professor at Cornell University who is collaborating on this research. "Applying AI and machine learning to automate control processes will make this easier." Led by Fermi National Accelerator Laboratory, the resonance control project aims to develop artificial intelligence and machine learning algorithms to substantially improve particle accelerator performance, driving scientific discovery while saving significant amounts of money in operating costs. Several partner institutions, including other national labs, universities and industry, are also contributing to the project. "For decades, Fermilab has been a global leader in superconducting radio-frequency technology - building, designing and operating among the most sophisticated accelerators and making transformative technological breakthroughs," said Sam Posen, a senior scientist at Fermilab and project principal investigator. "Today, the lab is at the forefront of an exciting new era to use AI and machine learning to extend our scientific reach even further by improving resonance control in next-generation accelerators and accelerating the time to discovery. We're excited to take this next step," Posen added. Staying in Tune To make energy transfer to the beam as efficient as possible, each cavity is tuned to a specific frequency called a resonant frequency. Matching the cavity's resonant frequency to the delivery of radio-frequency energy allows a relatively small amount of input power to build up large amplitude fields. "While SRF cavities are incredibly efficient at speeding up particles using low power, shifts away from their correct frequencies consume power that could be better spent pushing the particle beam harder and furthering the discovery potential," said Liepe. Scientists use a fast-moving tuner attached to the cavity to squeeze it back to its resonant frequency. Applying AI and machine learning has the potential to significantly improve tuning precision, and the algorithms and models learn over time how to minimize disturbances and adapt as conditions change. AI can also tailor this learning and adaptation to each of the 100-plus cavities that a large accelerator contains. This can allow researchers to push the operation of particle accelerators, increasing maximum energy or letting them turn down the power between beam pulses. Current and future SRF-based particle accelerators, like Fermilab's Proton Improvement Plan-II linear accelerator, SLAC National Accelerator Laboratory's Linac Coherent Light Source-SC, Brookhaven National Laboratory's Electron-Ion Collider, Michigan State University's Facility for Rare Isotope Beams and Argonne National Laboratory's Argonne Tandem Linac Accelerator System, or ATLAS, use SRF cavities to harness electromagnetic energy fed in from an outside source. Increasing Precision One beneficiary of this research is the PIP-II accelerator, which will provide the world's most intense neutrino beam for the Deep Underground Neutrino Experiment at the Long-Baseline Neutrino Facility, an international collaboration hosted by Fermilab. During its initial operation, PIP-II's particle beam will be sent as pulses, but the cavities are designed to operate at full electromagnetic field strength between pulses. Reducing the field between pulses would significantly reduce average power, cooling costs and equipment wear, but it would also make resonance control harder because of the frequency shifts as the cavity field is turned up and down. A key objective of this research is to test whether introducing AI to PIP-II's existing control equipment will provide enough precision to operate in pulsed mode - handling the cavity's frequency shifts, using less power and avoiding unplanned shutdowns known as trips. Creating a Common Framework In addition to cost savings and increased performance and reliability, this research aims to achieve other important objectives. One of these is to establish a common framework for resonance control data that can be shared across DOE laboratories and facilities and their partners. Enabling data sharing from different SRF-based accelerators can benefit the broader Genesis Mission research community. Today, every facility measures detuning in its own way, so the data can't be pooled, and a controller built for one machine may not work on the next. "The open-source low-level radio-frequency control system Berkeley Lab developed is the baseline for PIP-II and runs the cavities at the SLAC Accelerator Center's LCLS-II, said scientist Dan Wang, the project institutional lead from Lawrence Berkeley National Laboratory who also leads a related hardware-aware AI project. "The control system is used more widely still on conventional accelerators that do not rely on superconducting materials, such as the Argonne Wakefield Accelerator and our own Advanced Light Source." "An AI layer built on that platform benefits every facility running it," added Wang. "That is what we want to show the Genesis Mission community: better and lower-cost operation of SRF cavities, but also how AI can close fast control loops on real hardware. It is the right demonstration, and with all the labs working on it together, the right moment." Benefiting Society Another objective is to build a workforce pipeline at the intersection of AI/machine learning and low-level radio-frequency engineering. This will help train the scientists, engineers and technicians to design and operate the precise control electronics used in particle accelerators - a highly specialized field that is currently facing a talent shortage. By collaborating with industry partners, innovations can be applied in ways that directly benefit society and the U.S. economy. For example, industry partner xLight has developed a free-electron laser system, using particle accelerator technology pioneered at DOE national laboratories, to transform semiconductor manufacturing. "This research exemplifies the Genesis Mission goal to use AI to dramatically improve our scientific tools so that we can innovate better, faster and more efficiently," said Anna Grassellino, chief technology officer and associate laboratory director for the Technology Directorate at Fermilab. "By collaborating with other national labs, universities and industry, we are fostering innovation and developing a skilled workforce at the intersection of AI and science and engineering. This research will cement the role of the United States as a world leader in particle accelerator technology as we build more powerful, more sophisticated, more reliable and more autonomous accelerators." Along with Fermilab, partnering institutions on this project include Lawrence Berkeley National Laboratory, SLAC National Accelerator Laboratory, Argonne National Laboratory, Cornell University, Michigan State University, Toyota Technological Institute at Chicago, University of Michigan, the High Energy Accelerator Research Organization in Japan and xLight Inc.

U.S. Department of Energy invests in Fermilab projects to accelerate AI-enabled scientific discovery. The awards will support Fermilab projects by leveraging AI to boost innovation and technology development, advance DOE's Genesis Mission and accelerate scientific discovery. Media contact. * Tracy Marc, Fermilab [email protected], 224-290-7803 The Department of Energy announced today the list of projects that will receive funding through the Genesis Mission: Transforming Science and Energy with AI. Fermi National Accelerator Laboratory was selected for a portfolio of initiatives, including the AI/ML project led by Fermilab and eight others in which the lab is a key collaborator. These awards position Fermilab as a strong contributor to the Genesis Mission and demonstrate the lab's leadership in applying artificial intelligence and advanced technologies to accelerate scientific discovery. "Fermilab is honored to receive this support for advancing artificial intelligence as part of DOE's Genesis Mission. This investment strengthens our ability to accelerate scientific discovery, develop next-generation technologies, and drive innovation at the frontiers of particle physics and advanced technology," Fermilab Director Norbert Holtkamp said. "We are proud to contribute our expertise to a mission that will help shape the next era of U.S. leadership in science and technology." The Fermilab-led project will develop AI/ML-based resonance control algorithms to enable high-reliability, low-cost accelerator operations that will substantially improve the performance of particle accelerators that drive discovery science. Several partner institutions, including national labs, universities and industry partner xLight Inc., are contributing to this project. The Genesis Mission is a historic national initiative led by the U.S. Department of Energy, which is building the world's most powerful integrated science discovery platform. By uniting government, industry, academia and philanthropy, it is accelerating breakthroughs in energy, scientific discovery and national security through a new platform that combines AI, supercomputing, quantum systems and advanced scientific instruments. The projects named today will receive funding through a Request For Applications (RFA) that was announced by DOE in March. The goal of the Phase I RFA awards is to identify promising pathways toward transformative scientific capabilities and establish a foundation for future investment and scale. Fermilab project teams will design and demonstrate research workflows that integrate AI with scientific investigation, while rigorously evaluating whether those approaches can accelerate discovery, improve predictive capabilities, enhance experimentation, or generate new scientific insights. Fermilab participated in today's Genesis Mission Summit as a recipient of a first-round Genesis Mission award, underscoring its leadership in advanced accelerator technologies as the Department of Energy announced the selected projects. The AI/ML algorithms that will emerge from the Fermilab-led project will increase science reach, improve stability for users, increase radio-frequency amplifier lifetime and reduce operating costs. Superconducting radio-frequency (SRF) cavities are electromagnetic resonators that transfer energy to beams in particle accelerators. Because they are extremely efficient resonators, they can be disturbed by small vibrations and pressure fluctuations. For current and future SRF-based accelerators like Fermilab's Proton Improvement Plan-II (PIP-II) linear accelerator, SLAC National Laboratory's Linac Coherent Light Source-SC, Brookhaven National Laboratory's Electron-Ion Collider (EIC), Michigan State University's Facility for Rare Isotope Beams, Argonne National Laboratory's Argonne Tandem Linac Accelerator System, and industrial accelerators, it is vitally important to have precise control of the cavity resonant frequency. "These awards recognize the tremendous opportunity to combine artificial intelligence with Fermilab's world-leading expertise in superconducting accelerator technology," said Anna Grassellino, chief technology officer and associate laboratory director for the Technology Directorate at Fermilab. "By developing AI-driven control of superconducting RF cavities, we can make future accelerators - such as our own PIP-II - more efficient, reliable, and autonomous, enabling higher scientific performance while reducing operational complexity. This is an important step toward a new generation of intelligent accelerator facilities that will power discoveries across particle physics and many other fields of science." Fermilab will contribute as a key collaborator on several other projects aligned with core research areas of the lab such as neutrino and collider physics, the muon-to-electron-conversion experiment (Mu2e), precision frontier, the Electron-Ion Collider and advanced scientific computing. The following projects were selected for funding through the Genesis Mission: * How AI will expedite discovery in highly complex electron-ion collider data streams Lead institution: Purdue University * Using an AI-driven approach to detect anomalies in the CMS Level-1 Scouting System Lead institution: University of Colorado * Standardizing and advancing high-performance computing workloads using AI Lead institution: University of Wisconsin-Madison * Using agentic workflows for the expedited search and discovery of charged lepton flavor violation in Mu2e Lead institution: University of South Carolina * Deployment of advanced AI accelerated systems to detect, identify, classify and communicate supernova events in real time for the Deep Underground Neutrino Experiment (DUNE) Lead institution: Duke University * The use of AI agents for high-energy physics simulations and analysis operations Lead institution: University of Alabama * Accelerating DUNE physics with AI to discover neutrino interaction uncertainties Lead institution: Florida State University * Physics-driven digital twins simulations for fusion magnet systems Lead institution: Lawrence Berkeley National Laboratory Fermilab is an international leader in particle accelerator science research, which generates massive streams of data for scientific discovery using accelerator and detector technologies. The lab's high-energy physics research creates unique opportunities for AI innovations. That is why researchers are developing and implementing AI tools to improve the precision of measurements, optimizing operations and accelerating discovery. Fermi National Accelerator Laboratory is America's national laboratory for particle physics and accelerator research. Fermi Forward Discovery Group manages Fermilab for the U.S. Department of Energy Office of Science. Visit Fermilab's website at www.fnal.gov and follow us on social media.