A two-step fused machine learning approach for the prediction of glass-forming ability of metallic glasses

Abstract

Metallic glasses (MGs) are often perceived as quintessential structural materials. However, the widespread application of MGs is hindered primarily by their limited glass-forming ability (GFA) for the manufacture of large-scale MGs. In this work, a two-step fused machine learning (ML) approach is proposed, aiming to provide an efficient tactic for the precise prediction of MGs with robust GFA. In our ML framework, alloy compositions are the only required inputs. Moreover, the dataset comprises alloys that can and cannot be cast into MGs. This departs from the conventional ML approach utilizing only a correct set of training data (i.e. alloys that can cast into MGs). The fusion algorithm is also employed to further improve the performance of ML approach. The critical casting sizes predicted by our ML model are in good agreement with those reported in experiments. This work has extensive implications for the design of bulk MGs with superior GFA.