{"id":28894,"date":"2023-01-16T15:21:02","date_gmt":"2023-01-16T15:21:02","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=28894"},"modified":"2023-01-16T15:21:02","modified_gmt":"2023-01-16T15:21:02","slug":"artificial-intelligence-facilitate-self-assembly-new-nanostructures","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/artificial-intelligence-facilitate-self-assembly-new-nanostructures\/28894\/","title":{"rendered":"Artificial Intelligence used to facilitate self-assembly of new nanostructures"},"content":{"rendered":"

Scientists at the U.S. Department of Energy\u2019s (DOE) Brookhaven National Laboratory have used Artificial Intelligence to rapidly discover new self-assembled nanostructures.<\/h2>\n

The team demonstrated that Artificial Intelligence<\/a> (AI) can be used to facilitate the self-assembly of new nanostructures. The new autonomous methods have led to the discovery of three new nanostructures, including a first-of-its-kind nanoscale \u2018ladder\u2019.<\/p>\n

The research, \u2018Autonomous discovery of emergent morphologies in directed self-assembly of block 3 copolymer blends<\/a>,\u2019 is published in the journal Science Advances<\/em>.<\/p>\n

The newly discovered structures were formed by self-assembly<\/h3>\n

Self-assembly is the process where a material\u2019s molecules organise themselves into unique patterns. Scientists at Brookhaven\u2019s Center for Functional Nanomaterials (CFN) are experts at directing the self-assembly process and have created templates for materials to form desirable arrangements for applications in microelectronics and catalysis. The newly discovered structures further widen the scope of self-assembly\u2019s applications.<\/p>\n

\u201cSelf-assembly can be used as a technique for nanopatterning, which is a driver for advances in microelectronics and computer hardware,\u201d said CFN scientist and co-author Gregory Doerk.<\/p>\n

\u201cThese 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.\u201d<\/p>\n

The team aims 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.<\/p>\n

\u201cThe fact that we can now create a ladder structure, which no one has ever dreamed of before, is amazing,\u201d said CFN group leader and co-author Kevin Yager.<\/p>\n

\u201cTraditional 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\u2019ve found that entirely new structures are possible.\u201d<\/p>\n

Blending self-assembling materials together has allowed for unique structures to be discovered, but has also generated new challenges. With many more factors 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, the team used a new AI capability \u2013 autonomous experimentation.<\/p>\n

Developing an AI framework to accelerate material discovery<\/h3>\n

Brookhaven scientists at CFN and the National Synchrotron Light Source II (NSLS-II) have been collaborating with the Center for Advanced Mathematics for Energy Research Applications (CAMERA) at DOE\u2019s Lawrence Berkeley National Laboratory to develop an AI framework that can autonomously define and perform all the steps of an experiment.<\/p>\n

CAMERA’s gpCAM algorithm drives the framework’s autonomous decision-making. The latest research is the team\u2019s first successful demonstration of the algorithm\u2019s ability to discover new materials.<\/p>\n

\u201cgpCAM is a flexible algorithm and software for autonomous experimentation,\u201d said Berkeley Lab scientist and co-author Marcus Noack. \u201cIt was used particularly ingeniously in this study to autonomously explore different features of the model.\u201d<\/p>\n

\u201cWith help from our colleagues at Berkeley Lab, we had this software and methodology ready to go, and now we\u2019ve successfully used it to discover new materials,\u201d Yager said. \u201cWe\u2019ve now learned enough about autonomous science that we can take a materials problem and convert it into an autonomous problem pretty easily.\u201d<\/p>\n

\"\"
\u00a9 shutterstock\/Production Perig<\/figcaption><\/figure>\n

First, the team developed a complex sample with a spectrum of properties for analysis. The sample was then fabricated using the CFN nanofabrication facility and carried out the self-assembly in the CFN material synthesis facility.<\/p>\n

\u201cAn old school way of doing material science<\/a> is to synthesise a sample, measure it, learn from it, and then go back and make a different sample and keep iterating that process,\u201d Yager said. \u201cInstead, we made a sample that has a gradient of every parameter we\u2019re interested in. That single sample is thus a vast collection of many distinct material structures.\u201d<\/p>\n

The team then brought the samples to NSLS-II, which generates ultrabright X-rays for studying the structure of materials.<\/p>\n

\u201cOne of the SMI beamline\u2019s strengths is its ability to focus the X-ray beam on the sample down to microns,\u201d said NSLS-II scientist and co-author Masa Fukuto.<\/p>\n

\u201cBy analysing how these microbeam X-rays get scattered by the material, we learn about the material\u2019s 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 spots to measure next to maximise the value of each measurement.\u201d<\/p>\n

As the sample was measured at the SMI beamline, the algorithm created a model of the material\u2019s numerous and diverse set of structures, without human intervention. With each subsequent X-ray measurement, the model updated itself, making every measurement more accurate.<\/p>\n

In just a few hours, the algorithm identified three key areas for researchers to study in more detail. These key areas were imaged with the CFN electron microscopy facility, which uncovered the rails and rungs of a nanoscale ladder, among other new features.<\/p>\n

It is estimated that the researchers would have needed a month to make this discovery using traditional methods, compared to the six hours taken for the experiment.<\/p>\n

\u201cAutonomous methods can tremendously accelerate discovery,\u201d Yager said. \u201cIt\u2019s essentially \u2018tightening\u2019 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.\u201d<\/p>\n

Future uses of the team\u2019s autonomous research method<\/h3>\n

\u201cMoving 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,\u201d Doerk said.<\/p>\n

Now, the team is applying its autonomous research method to more challenging material discovery problems in self-assembly. Autonomous discovery methods are adaptable and can be applied to nearly any research problem.<\/p>\n

\u201cWe are now deploying these methods to the broad community of users who come to CFN and NSLS-II to conduct experiments,\u201d Yager said. \u201cAnyone 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.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"

Scientists have used Artificial Intelligence to rapidly facilitate the self-assembly of new nanostructures.<\/p>\n","protected":false},"author":18,"featured_media":28897,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[24429],"tags":[570,833],"acf":[],"yoast_head":"\nArtificial Intelligence used to facilitate self-assembly of new nanostructures<\/title>\n<meta name=\"description\" content=\"Scientists have used Artificial Intelligence to rapidly facilitate the self-assembly of new nanostructures.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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