Visualization of Self-sorted Local Atomic Motifs in disordered solids

The structural descriptions of even the most basic amorphous materials are under considerable debate. In this work, an intuitive computational technique has been developed to construct 3D statistical density maps to directly visualize and identify local atomic structures from simple monatomic amorphous germanium (a-Ge) to complex multi-atom systems such as copper zirconium metallic glass. We show motifs in copper zirconium that are unresolvable through traditional tools such as Voronoi indexing. This self-sorted local atomic motif (SLAM) method builds upon the Kabsch algorithm incorporating techniques in computer vision to produce least-squares optimized 3D density maps. Simultaneously, the SLAM method incorporates self-contained categorization to define local motifs based upon atomic structures. We present the methodology of the SLAM method and also present resulting motifs comparing models a-Ge and demonstrate its broad capability on metallic glass.