AI Case Study
US Department of Energy's SLAC National Accelerator Laboratory accelerates discovery of metallic glass with machine learning by 200 times
Finding new metallic glass materials that are stronger and lighter than today's best steel is a long sought after goal. But the time to evaluate millions of possible combinations of atoms is very slow and only a few have made it to production. Using machine learning along with with experiments that quickly make and simultaneously screen hundreds of sample materials allowed the discovery of three new blends speeding up the process by 200 times.
Construction And Engineering
"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 this 200 times faster than it could be done before, they reported today in Science Advances.
'It typically takes a decade or two to get a material from discovery to commercial use,' said Northwestern Professor Chris Wolverton, an early pioneer in using computation and AI to predict new materials and a co-author of the paper. '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, he said, is to get to the point where a scientist could 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.'
The team "discovered three new blends of ingredients that form metallic glass, and to do this 200 times faster than it could be done before."
"'While other groups have used machine learning to come up with predictions about where different kinds of metallic glass can be found', said Apurva Mehta, a staff scientist at SSR. '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.'"
"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."
R And D
Core Research And Development
"Blend two or three metals together and you 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 that's amorphous, with its atoms 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, plus it stands up better to corrosion and wear.
Even though 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."
"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."