Data-mining for hidden order in metallic liquids and glasses

Although metallic liquids and glasses look quite homogenous macroscopically, most of them exhibit structural and chemical orders at the atomic scale. This short-range (SRO) or medium-range order (MRO) occurs on a length scale of 5-20 {\AA}. However, they are generally difficult to discern at the macroscopic scale due to random orientations of the ordered units. In this paper, we develop an efficient computational algorithm to align the neighborhood cluster around each atom to reveal the hidden symmetry and order contained in the system. In our alignment algorithm, we put the center atoms into a common origin and rigidly rotate the clusters to maximize their common registry to reveal any existing SRO or MRO. The results determine what are the major competing orders and the strengths of various orders in the system. Such atomic scale information are very difficult to acquire by experiments and are critical for understanding the mechanism of glass formation and phase selections during the rapid solidification from the metallic liquids.