Everyone knows that the Computer—an artificial intelligence (AI)-like entity—on a Star Trek spaceship does everything from brewing tea to compiling complex analyses of flux data. But how are they used at real research facilities? How can AI agents—computer programs that can act based on a perceived environment—help scientists discover next-generation batteries or quantum materials? Three staff members at the National Synchrotron Light Source II (NSLS-II) described how AI agents support scientists using the facility’s research tools. As a U.S. Department of Energy’s (DOE) Office of Science user facility located at DOE’s Brookhaven National Laboratory, NSLS-II offers its experimental capabilities to scientists from all over the world who use it to reveal the mysteries of materials for tomorrow’s technology.
From improving experimental conditions to enhancing data quality, Andi Barbour, Dan Olds, Maksim Rakitin, and their colleagues are working on various AI projects at NSLS-II. A recent overview publication in Digital Discovery outlines several—but not all—ongoing AI projects at the facility.
First contact with AI
While movies often show AI agents as sentient super computers that can perform various tasks, real-world AI agents differ greatly from this portrayal.
“What we mean when we say AI is that we come up with an algorithm or a method—basically some mathematical process—that is going to do a ‘thing’ for us, such as classifying, analyzing, or making decisions, but we’re not going to hardcode the logic,” explained Olds, a physicist who works at one of NSLS-II’s scientific instruments that enables a wide range of research projects. The instruments at NSLS-II are called beamlines because they are a combination of an x-ray beam delivery system and an experimental station.
Rakitin, a physicist specialized in developing software to collect or analyze data at NSLS-II, added, “Instead of giving the program—the AI agent—a model, it builds its own model through training. If we want it to recognize a cat, we show it a cat instead of explaining that it is a furry animal with four legs, pointy ears, a tail, and so on. The program has to figure out how to identify a cat by itself.”
Researchers at facilities such as NSLS-II have two main reasons for adapting AI agents to their needs: the sheer volume of data and its complexity. Twenty years ago, it took several minutes to snap a data image—such as a diffraction pattern—of a battery. Now, at the beamline Olds works at, they can take the same shot in a fraction of a second. While this allows more research to happen at the beamline, it outpaces the traditional strategies used to analyze the data.
Barbour, a chemical physicist, faces the second challenge, complex data, in her work studying dynamics in quantum materials. Together with her collaborators, she investigates how the atomic and electronic order in these materials evolve under variable conditions.
“When we do experiments at the beamline, we are looking for correlations and patterns in the data over time. So, if we would need to write one long program that captures all the possibilities of our experiments, it would be incredibly complicated, hard to read, terrible to maintain, and a nightmare to automate. But an AI tool can learn how to handle our complex data without the need to explain every detail to the agent,” Barbour said.
Read more on the Brookhaven National Laboratory website