A machine learning approach for engineering bulk metallic glass alloys

Abstract Bulk metallic glasses (BMGs) are a unique class of materials that are gaining traction in a wide variety of applications due to their attractive physical properties. One limitation to the wide-scale use of these materials is the lack of predictable tools for understanding the relationships between alloy composition and ideal properties. To address this issue, we developed a framework for designing metallic glasses using machine learning (ML) models that predict three key properties of candidate BMG compositions: ability to exist in an amorphous state, critical casting diameter ( D m a x ), and supercooled liquid range ( Δ T x ). Our models take only the composition of the alloy as input, and were created from a database of more than 8000 metallic glass experiments assembled from several dozen papers and handbooks. We employed these ML models to optimize the properties of existing commercial alloys and found, experimentally, several of our ML-predicted compositions can form glasses and exceed existing alloys in one of our two design variables, Δ T x .