Batavia, IL – Fermilab in Chicago’s western suburbs has been selected for nine U.S. Department of Energy projects using artificial intelligence to improve particle accelerators and accelerate scientific discovery.
According to Fermilab, the Energy Department announced the awards Wednesday, July 22, 2026, through its Genesis Mission, a national initiative bringing together AI, supercomputing, quantum systems and advanced scientific instruments.
The Batavia laboratory will lead one artificial intelligence and machine-learning project while serving as a key collaborator on eight others.
Fermilab’s lead project will develop AI-based resonance-control algorithms intended to make particle accelerator operations more reliable and less expensive. The technology will help stabilize superconducting radio-frequency cavities, extend amplifier life and reduce operating costs.
Those cavities transfer energy to particle beams but can be disrupted by vibrations and pressure changes. Fermilab said improved controls could benefit current and future accelerator facilities at national laboratories, universities and industrial sites.
Other selected projects include AI-assisted anomaly detection for the CMS experiment, high-performance computing improvements and new tools for the Mu2e experiment’s search for charged-lepton flavor violation.
Additional collaborations will explore real-time supernova detection for the Deep Underground Neutrino Experiment, AI agents for high-energy physics simulations, neutrino research and digital-twin modeling for fusion magnet systems.
Fermilab Director Noemi Holtkamp said the investment strengthens the laboratory’s ability to accelerate discovery and develop next-generation technology.
Anna Grassellino, Fermilab’s chief technology officer, said AI-driven controls could make future accelerators more efficient, reliable and autonomous while allowing scientists to study increasingly complex questions.
The release did not specify individual award amounts. Fermilab said the teams will evaluate whether AI can improve predictions, experimentation and scientific workflows during the Genesis Mission’s first phase.
