A team of U.S researchers have made use of AI to discover and improve metallic glass at a fraction of its actual time and cost.
According to the scientists from Northwestern University, the Department of Energy’s SLAC National Accelerator Laboratory and the National Institute of Standards and Technology (NIST), machine learning algorithms could help pinpoint new materials 200 times faster than previously possible.
Metallic glass has shown a lot of promise as a protective coating and alternative to steel. However 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.
The research group took advantage of a system at SLAC’s Stanford Synchrotron Radiation Lightsource (SSRL) that combines machine learning 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.
According to Northwestern’s McCormick School of Engineering Professor and main author Chris Wolverton, the ultimate goal 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.
In the metallic glass study, the research team investigated thousands of alloys that each contain three cheap, non-toxic 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.
By the experiment’s third and final round, paper co-author and staff scientist at SSRL, Apurva Mehta, said that 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.
Content and image: SLAC/Northwestern University