Update paper.md

---

title: 'GB\_code: A grain boundary generation code'

title: 'GB_code: A grain boundary generation code'

tags:

  - Python

  - grain boundary

  - crystallography

  - CSL

 	- atomistic

  - atomistic simulations

authors:

  - name: R. Hadian

    orcid: 0000-0002-9616-4602

To simulate grain boundary properties using atomistic codes such as ``LAMMPS``[@LAMMPS], 

the atomic structure of the GBs need to be generated first. These geometrical entities have 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 GBs (currently for 

mathematical framework. ``GB_code`` uses the CSL construction to generate GBs (currently for 

cubic materials) systematically and provides input atomic structures for atomistic calculations. 

In addition to this final goal, the ``csl\_generator.py`` script and the attached Jupyter notebooks

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 

``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 

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. Some examples of the usage of the ``GB\_code`` in our previous publications have been shown 

and visualized. Some examples of the usage of the ``GB_code`` in our previous publications have been shown 

as well.

``GB\_code``uses the analytical and mathematical formulations of the following works 

``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 because of its extensive usage of python Numpy library

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.