About this Abstract |
| Meeting |
MS&T26: Materials Science & Technology
|
| Symposium
|
Grain Boundaries, Interfaces, and Surfaces: Fundamental Structure-Property-Performance Relationships
|
| Presentation Title |
Ab Initio Informed Prediction of Grain Boundary Energy in MgO |
| Author(s) |
Andrew Douglas Timmins, Evan Walter Clark Spotte-Smith, Rachel C Kurchin |
| On-Site Speaker (Planned) |
Andrew Douglas Timmins |
| Abstract Scope |
Grain boundary (GB) energy is a significant driver of GB migration and microstructural evolution in polycrystalline materials. Accurately representing the full five degree-of-freedom grain boundary energy distribution (GBED) is thus essential for predictive models of grain growth. This work presents a computational methodology for estimating GBEDs in polycrystalline systems, combining density functional theory (DFT) with fine-tuned machine-learned interatomic potentials (MLIPs). The approach is validated against an experimentally-derived GBED for MgO, obtained from triple junction measurements by Saylor, Morawiec, and Rohrer. Preliminary results demonstrate the utility of this approach within constrained sub-regions of MgO GB parameter space. This framework offers a scalable path toward kinetically- and thermodynamically-informed predictions of microstructural evolution across diverse materials systems. |