Computer, Is My Experiment Finished?

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 BarbourDan OldsMaksim 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

Image: From left to right: Andi BarbourMaksim Rakitin, and Dan Olds on the balcony overseeing the experimental floor of the National Synchrotron Light Source II

AI Agent Helps Identify Material Properties Faster

High-throughput X-ray diffraction measurements generate huge amounts of data. The agent renders them usable more quickly.

Artificial intelligence (AI) can analyse large amounts of data, such as those generated when analysing the properties of potential new materials, faster than humans. However, such systems often tend to make definitive decisions even in the face of uncertainty; they overestimate themselves. An international research team has stopped AI from doing this: the researchers have refined an algorithm so that it works together with humans and supports decision-making processes. As a result, promising new materials can be identified more quickly.

A team headed by Dr. Phillip M. Maffettone (currently at National Synchrotron Light Source II in Upton, USA) and Professor Andrew Cooper from the Department of Chemistry and Materials Innovation Factory at the University of Liverpool joined forces with the Bochum-based group headed by Lars Banko and Professor Alfred Ludwig from the Chair of Materials Discovery and Interfaces and Yury Lysogorskiy from the Interdisciplinary Centre for Advanced Materials Simulation. The international team published their report in the journal Nature Computational Science from 19 April 2021.

Read more on the BNL website

Image: Daniel Olds (left) and Phillip M. Maffettone working at the beamline.

Credit: BNL