Short-range order structure motifs learned from an atomistic model of a Zr50Cu45Al5 metallic glass. (15th August 2019)

• Record Type: Journal Article
• Authors: Maldonis, Jason J.; Banadaki, Arash Dehghan; Patala, Srikanth; Voyles, Paul M.
• Is Part Of: Acta materialia. Volume 175(2019)
• Page Start: 35
• Page End: 45
• Publication Date: 2019-08-15
• Subjects: Simulation -- Machine learning -- Metallic glass -- Structure -- Short-range order -- Motif; Materials -- Periodicals; Materials science -- Periodicals; Materials -- Mechanical properties -- Periodicals; Metallurgy -- Periodicals; Chemistry, Inorganic -- Periodicals; 620.112
• DOI: 10.1016/j.actamat.2019.05.002
• Languages: English
• ISSNs: 1359-6454

Abstract

Abstract: The structural motifs of a Zr50 Cu45 Al5 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. Graphical abstract: Image 1