Brookhaven National Lab applies AI to make big experiments autonomous
As a young scientist experimenting with neutrons and X-rays, Kevin Yager often heard this mantra: “Don’t waste beamtime.” Maximizing productive use of the potent and popular facilities that generate concentrated particles and radiation frequently required working all night to complete important experiments. Yager, who now leads the Electronic Nanomaterials Group at Brookhaven National Laboratory’s Center for Functional Nanomaterials (CFN), couldn’t help but think “there must be a better way.”
Yager focused on streamlining and automating as much of an experiment as possible and wrote a lot of software to help. Then he had an epiphany. He realized artificial intelligence and machine-learning methods could be applied not only to mechanize simple and boring tasks humans don’t enjoy but also to reimagine experiments.
“Rather than having human scientists micromanaging experimental details,” he remembers thinking, “we could liberate them to actually focus on scientific insight, if only the machine could intelligently handle all the low-level tasks. In such a world, a scientific experiment becomes less about coming up with a sequence of steps, and more about correctly telling the AI what the scientific goal is.”
Yager and colleagues are developing methods that exploit AI and machine learning to automate as much of an experiment as possible. “This includes physically handling samples with robotics, triggering measurements, analyzing data, and – crucially – automating the experimental decision-making,” he explains. “That is, the instrument should decide by itself what sample to measure next, the measurement parameters to set, and so on.”
Read more on the Brookhaven website
Image: Example dataset collected during an autonomous X-ray scattering experiment at Brookhaven National Laboratory (BNL). An artificial intelligence/machine learning decision-making algorithm autonomously selected various points throughout the sample to measure. At each position, an X-ray scattering image (small squares) is collected and automatically analyzed. The algorithm considers the full dataset as it selects subsequent experiments.
Credit: Kevin Yager, BNL