| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Bulk Metallic Glasses XVIII
|
| Presentation Title |
Effective Quantification of Liquid Structure in Metallic Alloys and its Relation to Glass-Forming Ability |
| Author(s) |
Porter Weeks, Katharine Flores |
| On-Site Speaker (Planned) |
Porter Weeks |
| Abstract Scope |
Recent research has proposed that structural order present in the liquid is related to glass-forming ability (GFA) in metallic alloys, suggesting that a high degree of order in the metallic liquid makes crystallization easier. However, the effective quantification of this liquid structure is extremely challenging due to the inherent long-range disorder present. Voronoi tessellation is a common method for describing the short-range order of disordered systems. Voronoi indices describe the topology of the polyhedral volume associated with a particular atom, but provide little insight into the relative similarity of the various polyhedra that make up the material. A more rigorous approach would ask if local atomic environments are similar enough to be considered the same structure. Here, we show that a machine-learning clustering approach (HDBSCAN) provides such characterization of the liquid structure. Through analysis of simulated Cu-Zr and Al-Sm alloys, we show that liquid order is inversely correlated to GFA. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Modeling and Simulation, Machine Learning, Solidification |