SLAC to Build ‘Self-Driving’ Lab with DOE Funding to Tame X-Ray Data Overload

SLAC National Accelerator Laboratory has received U.S. Department of Energy funding to develop SPIRE, a "self-driving" laboratory platform. This initiative will leverage artificial intelligence to automate experiments, addressing the critical bottleneck of processing vast amounts of data generated by SLAC's X-ray laser.

Samuel Adebayo

October 11, 2026 · 4 min read

The U.S. Department of Energy has announced new funding for the SLAC National Accelerator Laboratory to spearhead a project that will build a "self-driving" laboratory platform. The initiative, known as SPIRE, will use artificial intelligence to automate experiments, aiming to solve a critical bottleneck where the lab's powerful X-ray laser generates scientific data faster than researchers can possibly process it.

This move signals a significant shift in how scientific research is conducted, moving beyond simple automation to create an autonomous system where AI agents can make decisions to achieve high-level scientific goals. According to Angelo Dragone, the SPIRE lead principal investigator and a professor of photon science, the new approach opens up entirely new ways of making discoveries and dramatically reduces the time needed for experimental insights.

The Data Deluge at SLAC's X-ray Laser

At the heart of the challenge facing SLAC is the immense volume of information produced by its advanced scientific instruments. The lab's X-ray laser facilities are designed to probe the fundamental properties of matter, but in doing so, they create a torrent of data that has outpaced the capacity for human analysis. This data overload has become a primary bottleneck, slowing the potential rate of discovery and limiting the types of questions scientists can practically investigate.

The SPIRE project is designed to directly confront this issue. By integrating artificial intelligence into the experimental workflow, the initiative aims to not only analyze data in real time but also use those results to intelligently guide the experiment as it happens. This creates a closed loop of data collection, analysis, and instrumental adjustment that operates at machine speed, a process far beyond human capability.

Automated vs. Autonomous: Understanding SPIRE's Innovation

The core innovation of the SPIRE project lies in its pursuit of an "autonomous" system, a concept that goes a step beyond traditional lab "automation." While an automated workflow simply follows a predetermined, fixed script, an autonomous system is designed to be goal-oriented and adaptive. The following table breaks down the key differences in the approach.

A comparison of the operational characteristics of a traditional automated laboratory workflow and the goal-driven autonomous system being developed by the SPIRE initiative.
Aspect	Automated Workflow	Autonomous System (SPIRE)
System Control	Follows a fixed, pre-programmed script without deviation.	Uses Super Intelligence (SI) agents that can make independent decisions.
Decision-Making	Executes a pre-set sequence of actions. Human intervention is required to change parameters.	SI agents understand high-level scientific goals and determine which tools, algorithms, and parameters to use to achieve them without manual human input.

Frederic Poitevin, head of AI for science and operations at SLAC's Linac Coherent Light Source (LCLS) and a co-principal investigator for SPIRE, explained the distinction. “Where an automatic workflow follows a fixed script, an autonomous system incorporates SI agents that understand high-level scientific goals and determine which tools, algorithms and parameters to use, without manual human intervention,” he said. SLAC has already demonstrated a proof of concept for this approach by using machine learning to tune an electron diffraction instrument, a task that previously took hours of expert human effort.