SLAC National Accelerator Laboratory is leveraging artificial intelligence to process massive datasets from its world-class scientific facilities, transforming how researchers discover new materials and understand fundamental physics. The lab's contribution to the Department of Energy's Genesis Mission involves building AI tools that turn raw experimental data into actionable scientific insights in real time, rather than waiting weeks or months for analysis.

Why Is SLAC's Data Challenge So Urgent?

SLAC operates two of the largest scientific data-generating facilities on Earth. The Linac Coherent Light Source (LCLS) fires up to one million X-ray pulses per second, each capturing snapshots of electrons, atoms, and molecules in motion. This ultrafast imaging generates up to 40 terabytes of data in a single day. Meanwhile, the NSF-DOE Vera C. Rubin Observatory in Chile will produce approximately 7 million science alerts per night during its 10-year survey, accumulating 30 petabytes of astronomical data.

Without AI, this volume of information would be impossible to process. "The Linac Coherent Light Source and the NSF-DOE Vera C. Rubin Observatory will collect data at speeds and volumes that humans cannot process in real time," explained Chris Tassone, SLAC associate lab director of Energy Sciences.

How Is AI Solving the Data Processing Problem?

SLAC researchers are implementing AI solutions across the entire discovery pipeline, from data collection through analysis. One key innovation is SYNAPS-I, a "one-click" data collection and processing pipeline enabled by AI at the Stanford Synchrotron Radiation Lightsource (SSRL). This system automatically captures experimental data, transfers it to Department of Energy high-performance computing facilities, and performs reconstruction, segmentation, and feature identification without manual intervention.

The lab is also rethinking how AI and machine learning analysis runs during experiments themselves. Rather than waiting for data to be processed offline, SLAC is deploying AI at the "edge," meaning directly within detector and instrument architectures. This includes embedded field-programmable gate arrays (FPGAs), analog computing in application-specific integrated circuits (ASICs), and quantum algorithms in hardware that enable real-time intelligence during data acquisition.

These innovations connect to the American Science Cloud (AmSC), a national infrastructure that links supercomputers, experimental facilities, AI tools, and datasets across the country. The SLAC Shared Science Data Facility (S3DF) serves as a hub for scientific data from more than two dozen Department of Energy Office of Science projects and hosts the U.S. Data Facility for Rubin Observatory.

What Practical Applications Are Already Emerging?

• Accelerator Optimization: Machine learning tools developed by SLAC and collaborators help operators tune electron beams faster, diagnose faults across hundreds of subsystems, and make informed decisions in real time. These AI tools are now in use at accelerators worldwide, from the largest research facilities to industrial and medical devices.
• Materials Discovery: Ultrafast X-ray experiments at LCLS are driving scientific discovery in materials, chemistry, and biology by providing unprecedented views of atoms and molecules in action, enabling researchers to identify new materials for batteries and catalysis.
• Astronomical Insights: The Rubin Observatory's AI-processed data will help scientists obtain new observations of billions of stars and galaxies while investigating the nature of dark matter, dark energy, and the origins of the universe.

SLAC's approach reflects a broader shift in how science operates. "In this era of rapid and expansive data collection, AI will necessarily augment the way science is done, the same way the microscope or the telescope has accelerated breakthroughs," Tassone noted.

Lisa Bonetti, associate lab director for Technology Innovation and head of SLAC's Integrated Scientific and Data-Intensive Computing Initiative, emphasized the scale of the challenge and opportunity. "The need to grapple with such enormous and varied datasets and the instruments that produce them will feed the AI revolution and lead to future technologies with broad societal benefits," she stated.

The Genesis Mission, announced by the Department of Energy in fall 2025, aims to transform how America conducts science and engineering by doubling productivity and impact within a decade. SLAC's expertise in particle physics, synchrotron radiation, and astronomical data analysis, combined with its partnerships with Stanford University and other institutions, positions the lab as a key partner in this national effort.