Artificial Intelligence Accelerates Discovery of Metallic Glass

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.

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 Northwestern University, the Department of Energy’s SLAC National Accelerator Laboratory and the National Institute of Standards and Technology (NIST) 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.

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