Purdue University Graduate School
2023.12.2 Amirreza Kazemi.pdf (3.07 MB)

Data-driven prediction of the structure-property relationships for grain boundaries in metallic alloys

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posted on 2024-01-09, 21:40 authored by amirreza kazemiamirreza kazemi

Nanocrystalline materials have unique properties such as high ultimate strength and superior hardness. However, they also exhibit some disadvantages, such as low thermal stability. An effective strategy to address this issue is alloying with other materials. Grain boundaries play a pivotal role in property prediction due to their orientation between grains and the complexity of their structure. The prediction of structure-property relationships for GBs with microstructural complexity represents a difficult challenge.

To understand the effects of dopants on the material properties of grain boundaries, we constructed some bicrystal models for Al and Mg-doped Al (Al-Mg) alloys. Findings from shearing simulations of these GBs indicate that the GB structure and dopant distribution can influence GB migration. Dopants inhibit GB migration at certain GBs, effectively reinforcing these GBs. Shear-coupled GB migration in pure Al, as well as dopant inhibition of GB Al-Mg alloys, both contribute to the mechanisms of GB migration.


Degree Type

  • Doctor of Philosophy


  • Mechanical Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Shengfeng Yang

Advisor/Supervisor/Committee co-chair

Kejie Zhao

Additional Committee Member 2

Marcial Gonzalez

Additional Committee Member 3

Jing Zhang

Additional Committee Member 4

Xiao Luo