BVlain: A Python repository from dembart

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.

Installation

!pip install bvlain

Examples

Percolation barriers

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

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)

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

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.