Identifying flow defects in amorphous alloys using machine learning outlier detection methods

摘要

Shear transformation zones (STZs) are widely believed to be the fundamental flow defects that dictate the plastic deformation of amorphous alloys. However, it has been a long-term challenge to characterize STZs and their evolutions by experimental methods due to transient nature. Here we first introduced a consistent, automated, robust method to identify STZs by linear based machine learning outlier detection algorithms. We exemplify these algorithms to identify the atoms of STZs in Cu64Zr36 metallic glass system, and verify this data-driven model with a physical based model. It is revealed that the average STZ size slightly increases with decreasing cooling rate. (C) 2020 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.