NSLS-II scientists are changing how many experiments run by employing a coordinated team of AI-powered robots
UPTON, N.Y. — To build the experimental stations of the future, scientists at 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, are learning from some of the challenges that face them today. As light source technologies and capabilities continue to advance, researchers must navigate increasingly complex workflows and swiftly evolving experimental demands.
To meet these challenges, a team of NSLS-II scientists is training a team of AI-driven collaborative robots. These agile, adaptable systems are being developed to quickly shift between tasks, adjust to different experimental setups, and respond autonomously to real-time data. By taking on work using learning processes rather than preprogrammed steps, much like a human researcher, these robots are helping scientists realize a future where these systems can be deployed on demand, empowering them to explore new possibilities and fully harness the facility’s cutting-edge capabilities to investigate everything from battery technologies to quantum materials.
The team has successfully demonstrated this technology by rapidly deploying a prototype of one of these robotic systems to run an autonomous experiment overnight. The setup included different-sized samples that were randomly placed in the experimental environment without any preprogrammed knowledge of their location. The simulated experiment proceeded for eight hours without errors, showcasing the potential for user-friendly, AI-driven robotic integration in scientific research. Their results were recently published in Digital Discovery.
“We’re envisioning a new path forward,” said Phillip Maffettone, a computational scientist in NSLS-II’s Data Science and Systems Integration (DSSI) division and lead author of the study. “This approach isn’t just about speeding up current experiments; it’s a roadmap for the next generation of beamlines — modular, intelligent, and deeply integrated with AI. We’re designing a system that dynamically adapts to user needs.”
Building an automation foundation
NSLS-II currently operates 29 beamlines, with three more under construction and several others in development. The range, complexity, and volume of experiments conducted across these beamlines presents a challenge: designing a system that can automate existing workflows while remaining flexible enough to adapt to new types of experiments and new beamlines as they come online.
The synchrotron community has already found a lot of success in automating macromolecular X-ray crystallography (MX) experiments using robotics. MX beamlines can now perform automated and semi-automated experiments that routinely reach 99.96% reliability, which has increased the throughput of MX experiments. At NSLS-II alone, almost 13,000 samples were mounted at the Highly Automated Macromolecular Crystallography (AMX) beamline over the past four months. The robotic systems used at these beamlines are very effective for MX samples, and the robots have inspired scientists to think about what a more modular system could look like as they developed ideas for new beamline designs.
Daniel Olds is the lead beamline scientist at the upcoming High Resolution Powder Diffraction (HRD) beamline at NSLS-II. The beamline’s design enables users to take fast, in situ measurements that reveal real-time material behaviors such as battery cycling, catalytic reactions, and phase transitions — an approach that demands an innovative, adaptable system tailored to custom sample environments.
“We’re tackling a challenge faced by many researchers: how do we get the most science out of a limited window of beam time?” Olds said. “With so many formats and such little time, managing these experiments becomes a high-stakes logistical sprint.”
To envision what future experiments could look like, Maffettone, Olds, and a team of scientists from DSSI studied current experiments that would benefit most from flexible automation. They focused on the Pair Distribution Function (PDF) beamline, where visiting scientists, particularly those studying battery materials, often arrive with hundreds of unique samples. These can range from powders in narrow capillaries to flat “coupons” and even full pouch cell batteries like those used in electric vehicles. Some must be measured while charging and discharging in real time.
Instead of working in a single geometry or setup, a “smart” robot would be able to quickly learn how to handle a wide variety of sample types that differ in shape, size, and weight, just as a human scientist would. This kind of adaptability would reduce downtime, enable continuous beamline operation, and free researchers to focus more on insights than logistics.
Take capillary samples, for example. These are typically mounted on T-shaped brackets that hold 10 to 30 capillaries each. Once loaded and aligned with the beam, the capillaries are scanned sequentially as the bracket moves vertically, allowing different regions of each sample to be measured and averaged for more reliable data. Scans are fast, with each bracket taking just five to 10 minutes, leaving users little time between sample changes. Currently, switching from a capillary containing battery material to an actual operando battery setup also requires stopping the experiment, opening the protective hutch, and manually swapping samples. An automated system could streamline these processes, but only if it’s intuitive and flexible.
For energy research in particular, this shift could be transformative. Progress in energy storage depends on the ability to screen new materials and quickly test them under real-world conditions with limited scheduled time at the beamline. Adaptive robotics at NSLS-II would dramatically accelerate that process, helping researchers develop the next generation of high-performance batteries for applications ranging from earbuds to electric vehicles.
This is only one example of the many types of experiments in several different fields that this kind of system is hoping to accelerate. As Maffettone explained, “The dream is to have smart robots that users can request on a per-beam-time basis. These applications are designed to be quickly deployed, removed, and redeployed based on the needs of the experiment while also being able to integrate AI-agent-driven automation techniques. Because of this, the robots we use would need to be light and portable, have a modular build, and plug into an accessible software infrastructure.”
Read more on NSLS-II website
Image: NSLS-II computational scientist Phillip Maffettone simulated an experimental setup to test AI-driven robotic automation.
Credit: Kevin Coughlin/Brookhaven National Laboratory
