Self Assembly Nanotechnology Patterns for Better Computers Got 100 Times Faster

In early 2023, scientists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory used autonomous methods can discover new materials. The artificial intelligence (AI)-driven technique led to the discovery of three new nanostructures, including a first-of-its-kind nanoscale ladder.

Scanning-electron microscopy images depict novel nanostructures discovered by artificial intelligence. Researchers describe the patterns as skew (left), alternating lines (center), and ladder (right). Scale bars are 200 nanometers.

Their discovery of the nanoscale ladder and other new structures further widens the scope of self-assembly’s applications.

“Self-assembly can be used as a technique for nanopatterning, which is a driver for advances in microelectronics and computer hardware,” said 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.”

“The fact that we can now create a ladder structure, which no one has ever dreamed of before, is amazing,” said 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 leveraged a new AI capability: autonomous experimentation.

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 synthesize a sample, measure it, learn from it, and then go back and make a different sample and keep iterating that process,” Yager said. “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.”

Then, the team brought the sample to NSLS-II, which generates ultrabright x-rays for studying the structure of materials. CFN operates three experimental stations in partnership with NSLS-II, one of which was used in this study, the Soft Matter Interfaces (SMI) beamline.

“One of the SMI beamline’s strengths is its ability to focus the x-ray beam on the sample down to microns,” said NSLS-II scientist and co-author Masa Fukuto. “By analyzing how these microbeam x-rays get scattered by the material, we learn about the material’s local structure at the illuminated spot. Measurements at many different spots can then reveal how the local structure varies across the gradient sample. In this work, we let the AI algorithm pick, on the fly, which spot to measure next to maximize the value of each measurement.”

In a matter of hours, the algorithm had 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.