Short-Range Order Structure Motifs Learned from an Atomistic Model of a Zr$_{50}$Cu$_{45}$Al$_{5}$ Metallic Glass

Jason J. Maldonis; Arash Dehghan Banadaki; Srikanth Patala; Paul M. Voyles

doi:10.1016/j.actamat.2019.05.002

paper

Short-Range Order Structure Motifs Learned from an Atomistic Model of a Zr $_{50}$ Cu $_{45}$ Al $_{5}$ Metallic Glass

arXiv:1901.04124 · doi:10.1016/j.actamat.2019.05.002

Abstract

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.

Short-Range Order Structure Motifs Learned from an Atomistic Model of a Zr50​Cu45​Al5​ Metallic Glass

Abstract

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Short-Range Order Structure Motifs Learned from an Atomistic Model of a Zr $_{50}$ Cu $_{45}$ Al $_{5}$ Metallic Glass