Characterization of nanoscale structural heterogeneity in metallic glasses: A machine learning study

Abstract

Atomic force microscopy (AFM) is an efficient tool for studying the structural heterogeneity in metallic glasses (MGs). However, time-consuming analysis and limitations in the scanning process are downsides of this experiment. To tackle these problems, a machine learning (ML) model was developed to predict the distribution of energy dissipation on the MG surface with the increase in number of AFM scanning. The results indicated that it was possible to accurately predict the energy of scanning points, leading to a timesaving and reliable study. Moreover, characterization of structural heterogeneity shows that the viscoelastic response of each nanoscale region under sequences of AFM scans depends on the initial energy state. The predictive results unfold that the high-dissipated regions show a stochastic trend, while the regions with moderate energy level tend to exhibit a regular behavior. The low-dissipated regions also resist to the significant energy variations, which is due to their interconnected nanostructure.