Short-range order structure motifs learned from an atomistic model of a Zr50Cu45Al5 metallic glass
JJ Maldonis and AD Banadaki and S Patala and PM Voyles, ACTA MATERIALIA, 175, 35-45 (2019).
The structural motifs of a Zr50Cu45Al5 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. (C) 2019 Published by Elsevier Ltd on behalf of Acta Materialia Inc.
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