Engineering of Novel Fe-Based Bulk Metallic Glasses Using a Machine Learning-Based Approach

A broad range of potential chemical compositions makes difficult design novel bulk metallic glasses (BMGs) without performing expensive experimentations. To overcome this problem, it is very important to establish predictive models based on artificial intelligence. In work, a machine learning (ML) approach was proposed for predicting glass formation in numerous alloying and designing glassy alloys. The results showed that our ML model accurately predicted the critical thickness MGs. As case study, ternary Fe–B–Co system selected effects minor additions Cr, Nb Y with different atomic percentages were evaluated. It found addition leads significant improvement glass-forming ability (GFA) system; however, shift optimized composition occurred. experimental selective also confirmed capability Fe-based BMGs.