Extracting governing system for the plastic deformation of metallic glasses using machine learning

This paper shows hidden information from the plastic deformation of metallic glasses using machine learning. Ni 62 Nb 38 (at.%) metallic glass (MG) film and Zr 64.13 Cu 15.75 Al 10 Ni 10.12 (at.%) BMG, as two model materials, are considered for nano-scratching and compression experiment, respectively. The interconnectedness among variables is probed using correlation analysis. The evolvement mechanism and governing system of plastic deformation are explored by combining dynamical neural networks and sparse identification. The governing system has the same basis function for different experiments, and the coefficient error is ≤ 0.14% under repeated experiments, revealing the intrinsic quality in metallic glasses. Furthermore, the governing system is conducted based on the preceding result to predict the deformation behavior. This shows that the prediction agrees well with the real value for the deformation process.