Project Highlight - Grain boundary motion in FCC crystals: a molecular dynamics study

The completion of this project, which was conducted over two summers as a researcher at Lawrence Livermore National Laboratory, marked the completion of my MS in Materials Science from UCLA.

Completed for
UCLA & Lawrence Livermore Lab
Year
Focus area
Molecular dynamics simulations

Overview

My Master's Thesis at UCLA, supported by my research internship at Lawrence Livermore National Lab, investigated the structural dependence of shear-coupled grain boundary (GB) motion in face-centered cubic (FCC) copper using molecular dynamics (MD) simulations. The study focused on symmetrical tilt grain boundaries, exploring how atomic structure and dynamic phase transitions influence mechanical properties such as shear strength, coupling behavior, and deformation mechanisms. By employing evolutionary search algorithms (USPEX) to predict low-energy GB phases, the research identified novel structures like split kites (SK) and extended kites (EK), which deviate from traditional models. Simulations revealed that GB phases exhibit distinct coupling modes-(100) or (110)-depending on misorientation angle and temperature, with dynamic transitions between modes causing abrupt changes in shear stress and motion direction. Notably, low-angle boundaries demonstrated a unique "grain boundary twinning" mechanism, where shear stress triggered the nucleation of a new grain, enabling plasticity akin to deformation twinning.

Key findings highlighted the critical role of GB atomic structure in determining mechanical response. For example, SK structures favored the (100) coupling mode even at high angles, while normal kites (NK) adhered to the (110) mode. Temperature significantly influenced phase stability, with structural transitions altering coupling behavior and shear strength. The work also showcased the interplay between computational methods and materials science, using tools like LAMMPS for MD simulations and Ovito for visualization. These insights underscored the potential for tailoring material properties by controlling GB phases during processing, offering pathways to design stronger, more ductile polycrystalline materials.

View the full document here.

Tools used

  • Python
  • scikit-learn
  • LAMMPS
  • Ovito
  • HPC Resources
  • LaTeX

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