Nanostructures are engineered structures with features at the nanoscale between 1 and 100 nanometers. They include nano-textured surfaces, nanoparticles and nanotubes, as well as more complex nanoscale structures. Anything smaller than a nanostructure would be a simple molecule or atom. 

Scientists at the US Department of Energy's (DOE) Brookhaven National Laboratory have successfully demonstrated that AI-guided autonomous methods can discover new materials.  

The AI-driven technique led to the discovery of three new nanostructures, including a first-of-its-kind nanoscale "ladder". The structures were formed by a process called self-assembly, in which molecules organize themselves into unique patterns.  

Understanding nanostructures 

Carbon nanotubes are a popular kind of nanostructure. They are formed from a layer of graphene rolled into a tube with the hexagonal molecule structure continuous around the diameter. Similarly, nanofibers are strands of polymer with nanoscale diameters. They may be organic polymers such as silk or keratin or synthetic polymers such as polyurethane or polylactic acid (PLA). 

Nanostructures may be formed by top-down bulk processes, which, at their simplest, maybe just the successive breaking down of particles into nanoparticles. At the other extreme, they may be constructed in a bottom-up fashion, using scanning electron microscopes (SEM) or scanning tunnelling microscopes (STM). 

In the semiconductor industry, nanofabrication is widely used to mass-produce precisely defined nanostructures.  

Kevin Yager (left) and Gregory Doerk

Widened application  

Scientists at Brookhaven's Center for Functional Nanomaterials (CFN) are experts in the self-assembly process, creating templates for materials to form desirable arrangements for applications in microelectronics, catalysis, and more. The research report stated that the discovery of the nanoscale ladder and other new structures further widens the scope of self-assembly applications.  

In previous studies, scientists demonstrated that new patterns are made possible by blending two self-assembling materials. Staff scientists at CFN aim to build a library of self-assembled nanopattern types to broaden their applications. 

The Soft Matter Interfaces (SMI) beamline at the National Synchrotron Light Source II

Autonomous Experimentation 

Blending the self-assembling materials has enabled CFN scientists to uncover unique structures, but finding the right combination of parameters to create new and useful structures is a battle against time. Hence, to accelerate their research, the scientists unlocked a new AI capability, i.e., autonomous experimentation.  

By directing light beams, ranging from infrared to hard X-rays, toward samples at experimental stations (beamlines), National Synchrotron Light Source II (NSLS-II) at the US Department of Energy's (DOE) Brookhaven National Laboratory can reveal the electronic, chemical, and atomic structures of materials.

gpCAM algorithm 

In collaboration with the Center 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) 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. To accelerate materials discovery using their new algorithm, the team first developed a complex sample with a spectrum of properties for analysis.  

Stages of development 

• Properties for analysis: To accelerate materials discovery using their new algorithms, the team first developed a complex sample with a spectrum of properties for analysis. Researchers fabricated the sample using the CFN nanofabrication facility and performed the self-assembly in the CFN material synthesis facility. 
• Studying the structure: The team brought the sample to NSLS-II, which generates ultrabright X-rays for studying the structure of materials.  
• Updating the model: As the sample was measured at the SMI beamline, the model updated itself with each subsequent X-ray measurement, making every measurement more insightful and accurate. 

The experiment ran for about six hours. The researchers estimate they would have needed about a month to make this discovery using traditional methods. The team is now actively applying their autonomous research methods to even more challenging material discovery problems in self-assembly. These autonomous discovery methods are adaptable and can be applied to nearly any research problem. 

Source: Science Advances