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 A new paradigm of material identification based on graph theory
17.06.2019 | Science China Press

nachricht Electron beam strengthens recyclable nanocomposite
17.06.2019 | Kanazawa University

All articles from Materials Sciences >>>

The most recent press releases about innovation >>>

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

Im Focus: The hidden structure of the periodic system

The well-known representation of chemical elements is just one example of how objects can be arranged and classified

The periodic table of elements that most chemistry books depict is only one special case. This tabular overview of the chemical elements, which goes back to...

Im Focus: MPSD team discovers light-induced ferroelectricity in strontium titanate

Light can be used not only to measure materials’ properties, but also to change them. Especially interesting are those cases in which the function of a material can be modified, such as its ability to conduct electricity or to store information in its magnetic state. A team led by Andrea Cavalleri from the Max Planck Institute for the Structure and Dynamics of Matter in Hamburg used terahertz frequency light pulses to transform a non-ferroelectric material into a ferroelectric one.

Ferroelectricity is a state in which the constituent lattice “looks” in one specific direction, forming a macroscopic electrical polarisation. The ability to...

Im Focus: Determining the Earth’s gravity field more accurately than ever before

Researchers at TU Graz calculate the most accurate gravity field determination of the Earth using 1.16 billion satellite measurements. This yields valuable knowledge for climate research.

The Earth’s gravity fluctuates from place to place. Geodesists use this phenomenon to observe geodynamic and climatological processes. Using...

Im Focus: Tube anemone has the largest animal mitochondrial genome ever sequenced

Discovery by Brazilian and US researchers could change the classification of two species, which appear more akin to jellyfish than was thought.

The tube anemone Isarachnanthus nocturnus is only 15 cm long but has the largest mitochondrial genome of any animal sequenced to date, with 80,923 base pairs....

Im Focus: Tiny light box opens new doors into the nanoworld

Researchers at Chalmers University of Technology, Sweden, have discovered a completely new way of capturing, amplifying and linking light to matter at the nanolevel. Using a tiny box, built from stacked atomically thin material, they have succeeded in creating a type of feedback loop in which light and matter become one. The discovery, which was recently published in Nature Nanotechnology, opens up new possibilities in the world of nanophotonics.

Photonics is concerned with various means of using light. Fibre-optic communication is an example of photonics, as is the technology behind photodetectors and...

All Focus news of the innovation-report >>>

Anzeige

Anzeige

VideoLinks
Industry & Economy
Event News

SEMANTiCS 2019 brings together industry leaders and data scientists in Karlsruhe

29.04.2019 | Event News

Revered mathematicians and computer scientists converge with 200 young researchers in Heidelberg!

17.04.2019 | Event News

First dust conference in the Central Asian part of the earth’s dust belt

15.04.2019 | Event News

 
Latest News

Novel communications architecture for future ultra-high speed wireless networks

17.06.2019 | Information Technology

Climate Change in West Africa

17.06.2019 | Earth Sciences

Robotic fish to replace animal testing

17.06.2019 | Ecology, The Environment and Conservation

VideoLinks
Science & Research
Overview of more VideoLinks >>>