  The structural motifs of a Zr$_{50}$Cu$_{45}$Al$_{5}$ metallic glass were
learned from atomistic models using a new structure analysis method called
motif extraction that employs point-pattern matching and machine learning
clustering techniques. The motifs are the nearest-neighbor building blocks of
the glass and reveal a well-defined hierarchy of structures as a function of
coordination number. Some of the motifs are icosahedral or quasi-icosahedral in
structure, while others take on the structure of the most close-packed
geometries for each coordination number. These results set the stage for
developing clearer structure-property connections in metallic glasses. Motif
extraction can be applied to any disordered material to identify its structural
motifs without the need for human input.
