The National Synchrotron Light Source II (NSLS-II)—a U.S. Department of Energy (DOE) Office of Science user facility at DOE's Brookhaven National Laboratory—is among the world's most advanced synchrotron light sources, enabling and supporting science across various disciplines. Advances in automation, robotics, artificial intelligence (AI), and machine learning (ML) are transforming how research is done at NSLS-II, streamlining workflows, enhancing productivity, and alleviating workloads for both users and staff.
This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility: Unprocessed detector data of barium titanate from the PDF beamline (NSLS-II) are shown in four crystal phases: cubic, tetragonal, orthorhombic, and rhombohedral. (b) In the processed data from temperature series measurements, hierarchical clustering was able to group the data by crystal structure without prior phase input. Credit: IOPScience/Brookhaven National Laboratory As synchrotron facilities rapidly advance—providing brighter beams, automation, and robotics to accelerate experiments and discovery—the quantity, quality, and speed of data generated during an experiment continues to increase. Visualizing, analyzing, and sorting these large volumes of data can require an impractical, if not impossible, amount of time and attention. Presenting scientists with real-time analysis is as important as preparing samples for beam time, optimizing the experiment, performing error detection, and remedying anything that may go awry during a measurement. Addressing these challenges requires tools that are fast, adaptable, and applicable facility-wide. The rapid advancement of AI/ML provides the opportunity to optimize beamline operations, solve data challenges, and automate repetitive tasks with specialized applications created by NSLS-II staff and collaborators. Anomaly detection NSLS-II operates around the clock, and experiments run even when staff and users are not at the beamlines. They might be working on other aspects of their research—sample preparation, for example—or even sleeping as experiments run late into the night. Real-time quality control of an experiment is crucial, especially when measurements take several hours or even days. If a sample is damaged or misaligned, equipment fails, or any other unforeseen disturbance derails an experiment, the issue needs to be detected…