Abstract
Characterizing and predicting atoms prone to rearrangements directly from atomic structure are longstanding challenges in disordered systems. Here we report a successful identification of liquid-like atoms susceptible to rearranging in a model Cu50Zr50 metallic glassy system by machine-learning method. Moreover, we observe that there exists a characteristic length rc within the first neighbor shell, the liquid-like atoms behave more tightly packed inside a spherical region of the radius r<rc, but more loosely packed for r<rc. It shows that the local configurational anisotropy plays the key role for understanding the structural origin of the liquid-like atoms. Our study demonstrates that machine learning provides an unconventional tool to understand the intrinsic heterogeneities in metallic glassy system, and sheds light on the structural indicator of heterogeneous dynamic behaviors in disordered materials.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call