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 an essential focus of 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 an essential focus of materials science
and physics.
To simulate grain boundary properties using atomistic codes such as ``LAMMPS``[@LAMMPS],
the atomic structure of the GBs need to be generated first.
GBs are geometrical entities with a large parameter space that has been well formulated within a coincident site
lattice (CSL) mathematical framework. ``GB_code`` uses the CSL construction to generate GB atomic structures
(currently for cubic materials) systematically and provides input atomic structures for atomistic calculations
(e.g. ``LAMMPS``[@LAMMPS]). In addition to this final goal, 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.
``GB_code`` is mainly designed to be run in Linux terminal as it is documented in detail in the README
file of the repository but it can also be accessed via the attached Jupyter notebooks. The code consists
of two main scripts, ``csl_generator.py`` and ``gb_generator.py``, that should be used in this order to
produce the final GB structures. The attached Jupyter notebooks, ``Usage_of_GB_code.ipynb`` and
``Dichromatic_pattern_CSL_.ipynb``, can access the two scripts as modules, the former addresses the general
usage of the code with some extra tips and functions to locate GBs of interest, the latter depicts how CSL
properties such 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
the atomic structure of the GBs need to be generated first. GBs are geometrical entities with a large parameter space
that has been well formulated within a coincident site lattice (CSL) mathematical framework. ``GB_code`` uses the CSL
construction to generate GB atomic structures (currently for cubic materials) systematically and provides input atomic
structures for atomistic calculations (e.g. ``LAMMPS``[@LAMMPS]). 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.
``GB_code`` is mainly designed to be run in Linux terminal as it is documented in detail in the README file of the repository
but it can also be accessed via the attached Jupyter notebooks. The code consists of two main scripts, ``csl_generator.py`` and ``gb_generator.py``, that should be used in this order to produce the final GB structures. The attached Jupyter notebooks, ``Usage_of_GB_code.ipynb`` and ``Dichromatic_pattern_CSL_.ipynb``, can access the two scripts as modules, the former addresses the
general usage of the code with some extra tips and functions to locate GBs of interest, the latter depicts how CSL properties such
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 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
and is more comprehensive. The code has been designed to be simple to use and instructive with a special
attention to GB plane orientation.
attention to GB plane orientation which is often lacking.
