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

TL;DR

This paper introduces a novel machine learning-based motif extraction method to identify and analyze local structural motifs in a Zr50Cu45Al5 metallic glass, revealing hierarchical organization and diverse local geometries.

Contribution

It presents a new, automated motif extraction technique that uncovers local structural motifs in metallic glasses without human bias, advancing understanding of their atomic arrangements.

Findings

Identified hierarchical local motifs as a function of coordination number.

Discovered motifs include icosahedral and close-packed geometries.

Method applicable to any disordered material for structural analysis.

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.