Grain boundaries (GBs) are crystalline borders between single crystals in materials microstructure. They play an important
role in mechanical, chemical or electronic response of materials and are therefore essential to materials science and physics.
Grain boundaries (GBs) are crystalline borders between single crystals in materials microstructure. They play an important role in mechanical, chemical or electronic response of materials and are therefore essential to materials science and physics.
GBs are geometrical entities with a large parameter space that has been well formulated within a coincident site lattice (CSL) mathematical framework [@Sutton:1996]. One important computational advantage of the CSL formalism is that it enables the construction of GBs in a periodic setup for atomistic simulations. ``GB_code`` [@GB_code] uses the CSL construction to generate GB atomic structures (currently for cubic materials) systematically. It provides input atomic structures for large-scale atomistic simulations with interatomic potentials (as implemented e.g. in ``LAMMPS``[@LAMMPS]) or _ab initio_, density-functional-theory (DFT) simulations (as implemented e.g. in VASP [@VASP]). These atomistic codes can further calculate different properties of the GBs. In addition to providing the input structures, the ``csl_generator.py`` script and the attached Jupyter notebooks have extra functionality to show how the CSL properties can be used to locate, classify and categorize different GBs and to extract detailed information about them; which causes it to be a good interactive toolbox to learn about grain boundaries and versatile for running high-throughput calculations. The target audience are students/scientists of materials science and physics at any level of familiarity with the topic.
@@ -35,7 +34,7 @@ general usage of the code with some extra tips and functions to locate GBs of in
as the overlapping patterns and displacement shift complete (DSC) vectors can be extracted and visualized. In the notebooks, two examples of the usage of the ``GB_code`` in our previous publications[@Pub1, @Pub2] have been
shown as well.
``GB_code``uses the analytical and mathematical formulations of the following works [@Sutton:1996, @Bollmann:1984, @Grimmer]. Some functionality from this code on CSL [@Marcin] has been used in a modified form in our code. To our knowledge, in comparison to other GB generation codes in different scientific groups``GB_code``is relatively faster due its extensive usage of python Numpy library
``GB_code``uses the analytical and mathematical formulations of the following works [@Sutton:1996, @Bollmann:1984, @Grimmer]. Some functionality from this code [@Marcin] on CSL has been used in a modified form in the ``GB_code``. To our knowledge, in comparison to other GB generation codes in different scientific groups``GB_code``is relatively faster due its extensive usage of python Numpy library
and is more comprehensive. The code has been designed to be simple to use and instructive with a special
attention to GB plane orientation which is often lacking in other grain bundary creation codes.
