AI algorithm discovers new self-assembling nanostructures

US researchers at the Centre for Functional Nanomaterials have used AI to discover new self-assembled nanostructures, including a new nanoscale “ladder”

Scientists at the US Department of Energy's Brookhaven National Laboratory have demonstrated that artificial intelligence can be used to discover new materials. The AI-driven technique led to the discovery of three new nanostructures, including a first-of-its-kind nanoscale "ladder."

The newly discovered structures were formed through a process known as self-assembly, where a material's molecules organise themselves into unique patterns. The scientists at Brookhaven's Centre for Functional Nanomaterials (CFN) are experts in directing the self-assembly process, creating templates for materials to form desirable arrangements for applications in microelectronics, catalysis, and more. Their discovery of the nanoscale ladder and other new structures further widens the scope of self-assembly applications. This research opens up new possibilities for advancing microelectronics and other fields.

“Self-assembly can be used as a technique for nanopatterning, which is a driver for advances in microelectronics and computer hardware,” says CFN scientist and co-author Gregory Doerk. “These technologies are always pushing for higher resolution using smaller nanopatterns. You can get really small and tightly controlled features from self-assembling materials, but they do not necessarily obey the kind of rules that we lay out for circuits, for example. By directing self-assembly using a template, we can form patterns that are more useful.”

Staff scientists at CFN, which is a DOE Office of Science User Facility, aim to build a library of self-assembled nanopattern types to broaden their applications. In previous studies, they demonstrated that new types of patterns are made possible by blending two self-assembling materials together.

“The fact that we can now create a ladder structure, which no one has ever dreamed of before, is amazing,” says CFN group leader and co-author Kevin Yager. “Traditional self-assembly can only form relatively simple structures like cylinders, sheets, and spheres. But by blending two materials together and using just the right chemical grating, we’ve found that entirely new structures are possible.”

Blending self-assembling materials together has enabled CFN scientists to uncover unique structures, but it has also created new challenges. With many more parameters to control in the self-assembly process, finding the right combination of parameters to create new and useful structures is a battle against time. To accelerate their research, CFN scientists use the new AI capability of autonomous experimentation.

Accelerating materials discovery with algorithms

In collaboration with the Centre for Advanced Mathematics for Energy Research Applications (CAMERA) at DOE’s Lawrence Berkeley National Laboratory, Brookhaven scientists at CFN and the National Synchrotron Light Source II (NSLS-II), another DOE Office of Science User Facility at Brookhaven Lab, have been developing an AI framework that can autonomously define and perform all the steps of an experiment. CAMERA's gpCAM algorithm drives the framework's autonomous decision-making. The latest research is the team’s first successful demonstration of the algorithm’s ability to discover new materials.

“gpCAM is a flexible algorithm and software for autonomous experimentation,” says Berkeley Lab scientist and co-author Marcus Noack. “It was used particularly ingeniously in this study to autonomously explore different features of the model.”

“With help from our colleagues at Berkeley Lab, we had this software and methodology ready to go, and now we’ve successfully used it to discover new materials,” says Yager. “We’ve now learned enough about autonomous science that we can take a materials problem and convert it into an autonomous problem pretty easily.”

To accelerate materials discovery using their new algorithm, the team first developed a complex sample with a spectrum of properties for analysis. Researchers fabricated the sample using the CFN nanofabrication facility and carried out the self-assembly in the CFN material synthesis facility.

“An old school way of doing material science is to synthesise a sample, measure it, learn from it, and then go back and make a different sample and keep iterating that process,” says Yager. “Instead, we made a sample that has a gradient of every parameter we’re interested in. That single sample is thus a vast collection of many distinct material structures.”

As the sample was measured at the SMI beamline, the algorithm, without human intervention, created a model of the material’s numerous and diverse set of structures. The model updated itself with each subsequent x-ray measurement, making every measurement more insightful and accurate.

In a matter of hours, the algorithm identified three key areas in the complex sample for the CFN researchers to study more closely. They used the CFN electron microscopy facility to image those key areas in exquisite detail, uncovering the rails and rungs of a nanoscale ladder, among other novel features.

From start to finish, the experiment ran about six hours. The researchers estimate they would have needed about a month to make this discovery using traditional methods.

“Autonomous methods can tremendously accelerate discovery,” says Yager. “It’s essentially ‘tightening’ the usual discovery loop of science, so that we cycle between hypotheses and measurements more quickly. Beyond just speed, however, autonomous methods increase the scope of what we can study, meaning we can tackle more challenging science problems.”

“Moving forward, we want to investigate the complex interplay among multiple parameters. We conducted simulations using the CFN computer cluster that verified our experimental results, but they also suggested how other parameters, such as film thickness, can also play an important role,” says Doerk.

The team is actively applying their autonomous research method to even more challenging material discovery problems in self-assembly, as well as other classes of materials. Autonomous discovery methods are adaptable and can be applied to nearly any research problem.

“We are now deploying these methods to the broad community of users who come to CFN and NSLS-II to conduct experiments,” says Yager. “Anyone can work with us to accelerate the exploration of their materials research. We foresee this empowering a host of new discoveries in the coming years, including in national priority areas like clean energy and microelectronics.”

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