About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Mechanical Behavior at the Nanoscale VIII
|
| Presentation Title |
Atomistic Simulation and Machine Learning of the Mechanical Behavior of Σ5 Cu-Ag Grain Boundaries |
| Author(s) |
Shimanta Das, Chongze Hu |
| On-Site Speaker (Planned) |
Shimanta Das |
| Abstract Scope |
The segregation of solutes or impurities at grain boundaries (GBs) is a crucial interfacial phenomenon that significantly influences material properties in structural alloys. Using Cu-Ag as a model system, we performed molecular dynamics (MD) and hybrid Monte Carlo/MD (MC/MD) simulations to compare the segregation effect of Ag and the influence of GB structure on the mechanical properties of four distinct Cu Σ5 GB types. Although MD tensile tests and MC/MD simulations showed that the mixed-type GB has the highest tensile strength and toughness due to its low segregation tendency, we found that GB structural properties influence strength more than segregation. Dislocation analysis (DXA) revealed that deformation behavior remains nearly unchanged after Ag segregation to GBs. These findings suggest that when solutes have chemical properties similar to the matrix, their segregation to GBs has minimal effect on deformation behavior, and the intrinsic GB structural characteristics dominate the mechanical response. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Mechanical Properties, Modeling and Simulation |