BVlain is the module for bond valence site energy calculations and is about to solve tasks related to ionic conductivity of a tracer ion in a crystal structure.
For more details, see [documentation.](https://bvlain.readthedocs.io/en/latest/index.html)
## Installation
```python
!pip install bvlain
```
## Examples
##### Percolation barriers
```python
from bvlain import Lain
file = '/Users/artemdembitskiy/Downloads/LiFePO4.cif'
calc = Lain(verbose = False)
st = calc.read_file(file)
params = {'mobile_ion': 'Li1+', # mobile specie
'r_cut': 10.0, # cutoff for interaction between the mobile species and framework
'resolution': 0.2, # distance between the grid points
'k': 100 # maximum number of neighbors to be collected for each point
}
_ = calc.bvse_distribution(**params)
energies = calc.percolation_barriers(encut = 5.0)
for key in energies.keys():
print(f'{key[-2:]} percolation barrier is {round(energies[key], 4)} eV')
```
1D percolation barrier is 0.4395 eV
2D percolation barrier is 3.3301 eV
3D percolation barrier is 3.3594 eV
##### Percolation radii
```python
from bvlain import Lain
file = '/Users/artemdembitskiy/Downloads/LiFePO4.cif'
calc = Lain(verbose = False)
st = calc.read_file(file)
params = {'mobile_ion': 'Li1+', # mobile specie
'r_cut': 10.0, # cutoff for interaction between the mobile species and framework
'resolution': 0.2, # distance between the grid points
}
_ = calc.void_distribution(**params)
radii = calc.percolation_radii()
for key in radii.keys():
print(f'{key[-2:]} percolation barrier is {round(radii[key], 4)} angstrom')
```
1D percolation barrier is 0.3943 angstrom
2D percolation barrier is 0.2957 angstrom
3D percolation barrier is 0.1972 angstrom
##### Save volumetric data for visualization (.grd or .cube)
```python
from bvlain import Lain
file = '/Users/artemdembitskiy/Downloads/LiFePO4.cif'
calc = Lain(verbose = False)
st = calc.read_file(file)
params = {'mobile_ion': 'Li1+', # mobile specie
'r_cut': 10.0, # cutoff for interaction between the mobile species and framework
'resolution': 0.2, # distance between the grid points
'k': 100 # maximum number of neighbors to be collected for each point
}
_ = calc.bvse_distribution(**params)
_ = calc.void_distribution(**params)
calc.write_grd(file + '_bvse', task = 'bvse') # saves .grd file
calc.write_cube(file + '_void', task = 'void') # save .cube file
```
##### Bond valence sum mismatch
```python
from bvlain import Lain
file = '/Users/artemdembitskiy/Downloads/LiFePO4.cif'
calc = Lain(verbose = False)
st = calc.read_file(file)
dataframe = calc.mismatch(r_cut = 3.5)
```
For more examples, see [documentation.](https://bvlain.readthedocs.io/en/latest/index.html)
The library is under active development and it is not guaranteed that there are no bugs. If you observe not expected results, errors, please report an issue at github.
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"description": "\n\nBVlain is the module for bond valence site energy calculations and is about to solve tasks related to ionic conductivity of a tracer ion in a crystal structure.\n\nFor more details, see [documentation.](https://bvlain.readthedocs.io/en/latest/index.html)\n\n\n## Installation\n\n\n```python\n!pip install bvlain\n```\n\n## Examples\n\n##### Percolation barriers\n\n\n```python\nfrom bvlain import Lain\n\nfile = '/Users/artemdembitskiy/Downloads/LiFePO4.cif'\ncalc = Lain(verbose = False)\nst = calc.read_file(file)\n\nparams = {'mobile_ion': 'Li1+', # mobile specie\n\t\t 'r_cut': 10.0, # cutoff for interaction between the mobile species and framework\n\t\t 'resolution': 0.2,\t # distance between the grid points\n\t\t 'k': 100 # maximum number of neighbors to be collected for each point\n}\n_ = calc.bvse_distribution(**params)\nenergies = calc.percolation_barriers(encut = 5.0)\nfor key in energies.keys():\n print(f'{key[-2:]} percolation barrier is {round(energies[key], 4)} eV')\n```\n\n 1D percolation barrier is 0.4395 eV\n 2D percolation barrier is 3.3301 eV\n 3D percolation barrier is 3.3594 eV\n\n\n##### Percolation radii\n\n\n```python\nfrom bvlain import Lain\n\nfile = '/Users/artemdembitskiy/Downloads/LiFePO4.cif'\ncalc = Lain(verbose = False)\nst = calc.read_file(file)\n\nparams = {'mobile_ion': 'Li1+', # mobile specie\n\t\t 'r_cut': 10.0, # cutoff for interaction between the mobile species and framework\n\t\t 'resolution': 0.2,\t # distance between the grid points\n}\n_ = calc.void_distribution(**params)\nradii = calc.percolation_radii()\nfor key in radii.keys():\n print(f'{key[-2:]} percolation barrier is {round(radii[key], 4)} angstrom')\n```\n\n 1D percolation barrier is 0.3943 angstrom\n 2D percolation barrier is 0.2957 angstrom\n 3D percolation barrier is 0.1972 angstrom\n\n\n##### Save volumetric data for visualization (.grd or .cube)\n\n\n```python\nfrom bvlain import Lain\n\nfile = '/Users/artemdembitskiy/Downloads/LiFePO4.cif'\ncalc = Lain(verbose = False)\nst = calc.read_file(file)\n\nparams = {'mobile_ion': 'Li1+', # mobile specie\n\t\t 'r_cut': 10.0, # cutoff for interaction between the mobile species and framework\n\t\t 'resolution': 0.2,\t # distance between the grid points\n\t\t 'k': 100 # maximum number of neighbors to be collected for each point\n}\n_ = calc.bvse_distribution(**params)\n_ = calc.void_distribution(**params)\n\ncalc.write_grd(file + '_bvse', task = 'bvse') # saves .grd file\ncalc.write_cube(file + '_void', task = 'void') # save .cube file\n```\n\n##### Bond valence sum mismatch\n\n\n```python\nfrom bvlain import Lain\n\nfile = '/Users/artemdembitskiy/Downloads/LiFePO4.cif'\ncalc = Lain(verbose = False)\nst = calc.read_file(file)\ndataframe = calc.mismatch(r_cut = 3.5)\n```\n\n\nFor more examples, see [documentation.](https://bvlain.readthedocs.io/en/latest/index.html)\n\nThe library is under active development and it is not guaranteed that there are no bugs. If you observe not expected results, errors, please report an issue at github.\n\n\n\n\n\n\n",
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