Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

Artificial intelligence accelerates discovery of metallic glass

16.04.2018

Machine learning algorithms pinpoint new materials 200 times faster than previously possible

If you combine two or three metals together, you will get an alloy that usually looks and acts like a metal, with its atoms arranged in rigid geometric patterns.


With new, artificial intelligence approach, scientists discovered metallic glass 200 times faster than with an Edisonian approach.

Credit: SLAC National Accelerator Laboratory

But once in a while, under just the right conditions, you get something entirely new: a futuristic alloy called metallic glass. The amorphous material's atoms are arranged every which way, much like the atoms of the glass in a window. Its glassy nature makes it stronger and lighter than today's best steel, and it stands up better to corrosion and wear.

Although metallic glass shows a lot of promise as a protective coating and alternative to steel, only a few thousand of the millions of possible combinations of ingredients have been evaluated over the past 50 years, and only a handful developed to the point that they may become useful.

Now a group led by scientists at the Department of Energy's SLAC National Accelerator Laboratory, the National Institute of Standards and Technology (NIST) and Northwestern University has reported a shortcut for discovering and improving metallic glass -- and, by extension, other elusive materials -- at a fraction of the time and cost.

The research group took advantage of a system at SLAC's Stanford Synchrotron Radiation Lightsource (SSRL) that combines machine learning -- a form of artificial intelligence where computer algorithms glean knowledge from enormous amounts of data -- with experiments that quickly make and screen hundreds of sample materials at a time. This allowed the team to discover three new blends of ingredients that form metallic glass, and to do it 200 times faster than it could be done before.

The study was published today, April 13, in Science Advances.

"It typically takes a decade or two to get a material from discovery to commercial use," said Chris Wolverton, the Jerome B. Cohen Professor of Materials Science and Engineering in Northwestern's McCormick School of Engineering, who is an early pioneer in using computation and AI to predict new materials. "This is a big step in trying to squeeze that time down. You could start out with nothing more than a list of properties you want in a material and, using AI, quickly narrow the huge field of potential materials to a few good candidates."

The ultimate goal, said Wolverton, who led the paper's machine learning work, is to get to the point where a scientist can scan hundreds of sample materials, get almost immediate feedback from machine learning models and have another set of samples ready to test the next day -- or even within the hour.

Over the past half century, scientists have investigated about 6,000 combinations of ingredients that form metallic glass. Added paper co-author Apurva Mehta, a staff scientist at SSRL: "We were able to make and screen 20,000 in a single year."

Just getting started

While other groups have used machine learning to come up with predictions about where different kinds of metallic glass can be found, Mehta said, "The unique thing we have done is to rapidly verify our predictions with experimental measurements and then repeatedly cycle the results back into the next round of machine learning and experiments."

There's plenty of room to make the process even speedier, he added, and eventually automate it to take people out of the loop altogether so scientists can concentrate on other aspects of their work that require human intuition and creativity. "This will have an impact not just on synchrotron users, but on the whole materials science and chemistry community," Mehta said.

The team said the method will be useful in all kinds of experiments, especially in searches for materials like metallic glass and catalysts whose performance is strongly influenced by the way they're manufactured, and those where scientists don't have theories to guide their search. With machine learning, no previous understanding is needed. The algorithms make connections and draw conclusions on their own, which can steer research in unexpected directions.

"One of the more exciting aspects of this is that we can make predictions so quickly and turn experiments around so rapidly that we can afford to investigate materials that don't follow our normal rules of thumb about whether a material will form a glass or not," said paper co-author Jason Hattrick-Simpers, a materials research engineer at NIST. "AI is going to shift the landscape of how materials science is done, and this is the first step."

Experimenting with data

In the metallic glass study, the research team investigated thousands of alloys that each contain three cheap, nontoxic metals.

They started with a trove of materials data dating back more than 50 years, including the results of 6,000 experiments that searched for metallic glass. The team combed through the data with advanced machine learning algorithms developed by Wolverton and Logan Ward, a graduate student in Wolverton's laboratory who served as co-first author of the paper.

Based on what the algorithms learned in this first round, the scientists crafted two sets of sample alloys using two different methods, allowing them to test how manufacturing methods affect whether an alloy morphs into a glass. An SSRL x-ray beam scanned both sets of alloys, then researchers fed the results into a database to generate new machine learning results, which were used to prepare new samples that underwent another round of scanning and machine learning.

By the experiment's third and final round, Mehta said, the group's success rate for finding metallic glass had increased from one out of 300 or 400 samples tested to one out of two or three samples tested. The metallic glass samples they identified represented three different combinations of ingredients, two of which had never been used to make metallic glass before.

###

The study was funded by the US Department of Energy (award number FWP-100250), the Center for Hierarchical Materials Design and the National Institute of Standards and Technology (award number 70NANB14H012).

Amanda Morris | EurekAlert!
Further information:
https://news.northwestern.edu/stories/2018/april/artificial-intelligence-accelerates-discovery-of-metallic-glass/?stage=Live

More articles from Materials Sciences:

nachricht Graphene origami as a mechanically tunable plasmonic structure for infrared detection
25.04.2018 | University of Illinois College of Engineering

nachricht Scientists create innovative new 'green' concrete using graphene
24.04.2018 | University of Exeter

All articles from Materials Sciences >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: BAM@Hannover Messe: innovative 3D printing method for space flight

At the Hannover Messe 2018, the Bundesanstalt für Materialforschung und-prüfung (BAM) will show how, in the future, astronauts could produce their own tools or spare parts in zero gravity using 3D printing. This will reduce, weight and transport costs for space missions. Visitors can experience the innovative additive manufacturing process live at the fair.

Powder-based additive manufacturing in zero gravity is the name of the project in which a component is produced by applying metallic powder layers and then...

Im Focus: Molecules Brilliantly Illuminated

Physicists at the Laboratory for Attosecond Physics, which is jointly run by Ludwig-Maximilians-Universität and the Max Planck Institute of Quantum Optics, have developed a high-power laser system that generates ultrashort pulses of light covering a large share of the mid-infrared spectrum. The researchers envisage a wide range of applications for the technology – in the early diagnosis of cancer, for instance.

Molecules are the building blocks of life. Like all other organisms, we are made of them. They control our biorhythm, and they can also reflect our state of...

Im Focus: Spider silk key to new bone-fixing composite

University of Connecticut researchers have created a biodegradable composite made of silk fibers that can be used to repair broken load-bearing bones without the complications sometimes presented by other materials.

Repairing major load-bearing bones such as those in the leg can be a long and uncomfortable process.

Im Focus: Writing and deleting magnets with lasers

Study published in the journal ACS Applied Materials & Interfaces is the outcome of an international effort that included teams from Dresden and Berlin in Germany, and the US.

Scientists at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) together with colleagues from the Helmholtz-Zentrum Berlin (HZB) and the University of Virginia...

Im Focus: Gamma-ray flashes from plasma filaments

Novel highly efficient and brilliant gamma-ray source: Based on model calculations, physicists of the Max PIanck Institute for Nuclear Physics in Heidelberg propose a novel method for an efficient high-brilliance gamma-ray source. A giant collimated gamma-ray pulse is generated from the interaction of a dense ultra-relativistic electron beam with a thin solid conductor. Energetic gamma-rays are copiously produced as the electron beam splits into filaments while propagating across the conductor. The resulting gamma-ray energy and flux enable novel experiments in nuclear and fundamental physics.

The typical wavelength of light interacting with an object of the microcosm scales with the size of this object. For atoms, this ranges from visible light to...

All Focus news of the innovation-report >>>

Anzeige

Anzeige

VideoLinks
Industry & Economy
Event News

Invitation to the upcoming "Current Topics in Bioinformatics: Big Data in Genomics and Medicine"

13.04.2018 | Event News

Unique scope of UV LED technologies and applications presented in Berlin: ICULTA-2018

12.04.2018 | Event News

IWOLIA: A conference bringing together German Industrie 4.0 and French Industrie du Futur

09.04.2018 | Event News

 
Latest News

The dispute about the origins of terahertz photoresponse in graphene results in a draw

25.04.2018 | Physics and Astronomy

Graphene origami as a mechanically tunable plasmonic structure for infrared detection

25.04.2018 | Materials Sciences

First form of therapy for childhood dementia CLN2 developed

25.04.2018 | Studies and Analyses

VideoLinks
Science & Research
Overview of more VideoLinks >>>