July 21, 2026 feature AI agent helps prepare synchrotron X-ray experimental measurements, paving the way for autonomous operation Ingrid Fadelli Author Sadie Harley Scientific Editor Robert Egan Senior Editor Artificial intelligence (AI) models are now used daily by many people worldwide, both for professional and personal purposes. Over the past decades, scientists specialized in various disciplines have also started using these models to conduct research or simplify their experimental practices. Researchers at Stanford University and SLAC National Accelerator Laboratory recently explored the possibility of using an AI-powered agent to prepare a synchrotron-based X-ray experiment.

Synchrotrons are large research facilities at which electrons are accelerated to produce very bright X-rays, which can then be used to study the atomic structure of materials, molecules and biological samples. In a paper published in Nature Machine Intelligence, the team at Stanford and SLAC proposed using an AI-based agent to prepare a real synchrotron X-ray experiment. They showed that this agent could autonomously plan actions, interpret observations and generate instrument-control commands to complete sample alignment.

"Our work demonstrates an agentic AI X-ray scientist that can autonomously align single-crystal samples at synchrotron X-ray beamlines," Zhantao Chen, first author of the paper and now an assistant professor at The University of Texas at Austin, who led this work while at SLAC National Accelerator Laboratory and Stanford University, told Phys.org. "This agent can query experimental status, reason about what's going on and what needs to be done, and then carry out the experiment toward successful sample alignment. The original inspiration came from the fact that sample alignment is an important first step in almost every single-crystal synchrotron X-ray experiment, yet it can be tedious." The quest to automate X-ray sample alignment Chen and his colleagues initially set out to automate a specific process that scientists conducting synchrotron experiments often find tedious.

This is the alignment of a crystal sample to enable the collection of quality X-ray data. "We initially wanted to develop AI to automate this process," explained Chen. "As the project evolved, however, we became interested in a broader question: Can AI reason through and perform experimental tasks much like a human scientist at a real-world synchrotron beamline?

These two motivations—the practical need for automation and the more ambitious scientific question—together inspired this work." While this recent study primarily focused on the autonomous alignment of samples, the researchers wanted to use this specific task as an example of what AI agents, particularly large language models (LLMs), could achieve in experimental settings. Their demonstration shows that these agents could perform experimental tasks autonomously, adapting to unexpected or changing conditions. "Simply put, there is a collection of experimental tools that the AI agent can use, including those needed to perform the experiment, such as reading experimental logs, taking detector images and doing motor scans," said Chen.