Smarter Experiments for Faster Materials Discovery

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To use the decision-making algorithm for their measurements, the team needed to automate the measurement and also the data analysis. This image shows how all pieces are integrated with each other to form a closed looped. The algorithm receives analyzed data from the last measurement step, adds this data to its model, calculates the best next step, and sends its decision to the beamline to execute the next measurement.

How we got here

To make autonomous experiments a reality, the team had to tackle three important pieces: the automation of the data collection, real-time analysis, and, of course, the decision-making algorithm.

“This is an exciting part of this collaboration,” said Fukuto. “We all provided an essential piece for it: The CAMERA team worked on the decision-making algorithm, Kevin from CFN developed the real-time data analysis, and we at NSLS-II provided the automation for the measurements.”

The team first implemented their decision-making algorithm at the Complex Materials Scattering (CMS) beamline at NSLS-II, which the CFN and NSLS-II operate in partnership. This instrument offers ultrabright x-rays to study the nanostructure of various materials. As the lead beamline scientist of this instrument, Fukuto had already designed the beamline with automation in mind. The beamline offers a sample-exchanging robot, automatic sample movement in various directions, and many other helpful tools to ensure fast measurements. Together with Yager’s real-time data analysis, the beamline was—by design—the perfect fit for the first “smart” experiment.

The first “smart” experiment

The first fully autonomous experiment the team performed was to map the perimeter of a droplet where nanoparticles segregate using a technique called small-angle x-ray scattering at the CMS beamline. During small-angle x-ray scattering, the scientists shine bright x-rays at the sample and, depending on the atomic to nanoscale structure of the sample, the x-rays bounce off in different directions. The scientists then use a large detector to capture the scattered x-rays and calculate the properties of the sample at the illuminated spot. In this first experiment, the scientists compared the standard approach of measuring the sample with measurements taken when the new decision-making algorithm was calling the shots. The algorithm was able to identify the area of the droplet and focused on its edges and inner parts instead of the background.

“After our own initial success, we wanted to apply the algorithm more, so we reached out to a few users and proposed to test our new algorithm on their scientific problems,” said Yager. “They said yes, and since then we have measured various samples. One of the most interesting ones was a study on a sample that was fabricated to contain a spectrum of different material types. So instead of making and measuring an enormous number of samples and maybe missing an interesting combination, the user made one single sample that included all possible combinations. Our algorithm was then able to explore this enormous diversity of combinations efficiently,” he said.

What’s next?

After the first successful experiments, the scientists plan to further improve the algorithm and therefore its value to the scientific community.  One of their ideas is to make the algorithm “physics-aware”—taking advantage of anything already known about material under study—so the method can be even more effective. Another development in progress is to use the algorithm during synthesis and processing of new materials, for example to understand and optimize processes relevant to advanced manufacturing as these materials are incorporated into real-world devices. The team is also thinking about the larger picture and wants to transfer the autonomous method to other experimental setups. 

“I think users view the beamlines of NSLS-II or microscopes of CFN just as powerful characterization tools. We are trying to change these capabilities into a powerful material discovery facility,” Fukuto said.

This work was funded by the DOE Office of Science (ASCR and BES).

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