About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Chemistry and Physics of Interfaces
|
| Presentation Title |
Framework for Predictive Modelling of Grain Boundaries |
| Author(s) |
Samira Anker, Christopher Peter Race |
| On-Site Speaker (Planned) |
Samira Anker |
| Abstract Scope |
Metal grain boundaries (GB) are atomistic scale defects, with many possible structures for a pure metal and even more when impurities and alloying additions are present. The properties of GBs can be modelled using density functional theory (DFT) or molecular dynamics (MD), however, many simulations are required to study all the possible structures, and only a small subset of structures can be captured at DFT length scales.
In this work we perform both DFT and MD simulations to investigate 160 arrangements of aluminium GBs containing a single segregating copper atom. We calculate segregation energy and structural properties such as bond parameters and Voronoi volume to compare the geometries. We apply machine learning tools to develop and test predictive models for the GBs in this system and present our criteria for a framework to create robust, predictive models for a given GB system, whilst minimising the number of input simulations required. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Other, Modeling and Simulation, Machine Learning |