The increasing size and complexity of atomic-scale structures and crystals defects studied by molecular dynamics (MD) demand powerful analysis tools and indexing algorithms, which help to identify (and quantify) important features in large datasets and connect the atomistic description of defects to higher-scale models.
I talk about the development of new computational techniques that facilitate the transformation of the original atomistic representation of a solid into a more abstract, high-level description of a microstructure in terms of grains, interfaces, and other defects such as dislocation lines.
A key element in this transformation process is the robust identification of characteristic atomic arrangements as they occur in crystal defects such as stacking faults or coherent grain boundaries. Using advanced pattern matching algorithms, which take into account medium-range atomic order, the range of identifiable atomic structures can be greatly extended.
The automated identification of atomic structures provides the technical foundation for our definition of purely elastic deformation at the atomic level. Given such a definition, we can compute the incremental elastic and plastic deformation gradient fields Fe and Fp for a series of MD snapshots. The obtained fields and their variation with time can provide valuable insights into the number, mobility, and localization of defects, and link atomistics to continuum models of crystal plasticity.
As a recent application of this approach, I present the automated identification of grain boundary dislocations and the exact calculation of their partial Burgers vectors from the incompatible elastic displacement field that surrounds them.
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