Linking plastic heterogeneity of bulk metallic glasses to quench-in structural defects with machine learning

When metallic glasses are subjected to mechanical loads, the plastic response of atoms is heterogeneous. However, the degree to which the plastic units are correlated with the structural defects frozen in the quenched glass structure is still elusive. Here, we introduce a machine learning framework to predict the plastic heterogeneity of atoms in Cu-Zr metallic glasses solely from the undeformed, quenched configuration. We propose that an atomic-scale quantity, "quench-in softness", calibrated from a gradient boosted decision tree model trained on a set of short- and medium-range site features, can identify plastically susceptible sites at various strain levels with high accuracy. The predictive ability is further confirmed in that a model trained on a single composition and quench rate retains high accuracy on other compositions and quench rates without any further training. We also quantitatively assess historical site descriptors against our method, demonstrating that the regularity-related features introduced in this work are more predictive and may play an important role in future glass characterization. Our work presents a general, data-centric framework that could potentially be used to address the structural origin of any site-specific property in metallic glasses.