#### About
Perconeb is the library used for fast identifying of the optimal jumps mobile species required for the percolation in a crystal structure.
The identified jumps can be used for nudged elastic band (NEB) clalculation of the mobile species activation barriers.
Functionality includes:
- 1-3D percolation radius calculations (percolation threshold)
- cutoff search for the shortest ionic jumps between equilibrium positions
- identifying of unique jumps required for 1-3D percolation
- initial trajectory preparation for NEB calculations
#### Installation
!pip install perconeb
#### How to use
```python
from core import Perconeb
from ase.io import read
file = '/Users/artemdembitskiy/Desktop/perconeb/LiCoO2.cif'
atoms = read(file) # can be .cif, POSCAR, or any that is readable by ase
specie = 3 # mobile specie, atomic number
tr = 0.75 # minimum allowed distance between linear segment connecting i,j-th positions of mobile specie
# and the framework, angstrom
upper_bound = 10.0 # maximum allowed distance for intersite hops of mobile species, angstrom
calc = Perconeb(atoms, specie, upper_bound = upper_bound)
dim, cutoff = calc.percolation_dimensionality(tr = tr)
dimensions = {2: 1, 4: 2, 8: 3} # we use 2x2x2 supercell to evaluate the percolation dimensionality
# if there is a connection between some point in the unitcell
# and its closest replica within supercell then we have the percolation
# number of connected unitcell and dimensionality are related as follows
# 2 -> 1D, 4 -> 2D, 8 -> 3D
print(f'Maximum percolation dimnesionality is {dimensions[dim]}')
print(f'That requires mobile specie hops up to {round(cutoff, 3)} Angstorm')
```
Maximum percolation dimnesionality is 3
That requires mobile specie hops up to 4.922 Angstorm
#### Inequivalent mobile specie hops (edges)
```python
edges, edge_ids, inverse = calc.unique_edges(tr = tr, dim = dim, cutoff = cutoff)
edges # each edge is represented by a 5D vector [i, j, offset_x, offset_y, offset_z]
for edge in edges:
source, target = edge[:2] # indices of source and target mobile atoms in the mobile sublattice
offset = edge[2:]
p_source = atoms[atoms.numbers == specie].positions[source]
p_target = atoms[atoms.numbers == specie].positions[target]
distance_vector = p_source - (p_target + np.dot(atoms.cell, offset))
distance = np.linalg.norm(distance_vector)
print(f'Source {source}, target {target}, offset {offset}, hop distance {round(distance, 4)}')
```
Source 0, target 1, offset [0 0 0], hop distance 4.9124
Source 0, target 0, offset [ 1 -1 0], hop distance 4.7593
Source 0, target 0, offset [0 1 0], hop distance 2.4346
#### Initial gues for NEB
```python
sep_dist = 8.0 # minimum size of the supercell, Angstrom
idpp = False # whether to use IDPP or linear interpolation scheme
step = 1.0 # interpolation step, Angstroms
out = calc.neb_guess(edges, edge_ids,
min_sep_dist = sep_dist,
idpp = idpp,
step = step
)
calc.write_traj(out, f'{file}_traj.cif') # the whole trajectory can be visualized in jmol
```
#### Save percolating pathway within supercell
```python
from ase.io import write
file = '/Users/artemdembitskiy/Desktop/perconeb/LiCoO2.cif'
atoms = read(file)
specie = 3
calc = Perconeb(atoms, specie, upper_bound = 10.0)
dim, cutoff = calc.percolation_dimensionality(tr = 0.75)
mask = calc._filter_edges(tr = 0.75, cutoff = cutoff)
sublattice = calc.mobile_supercell
base = sublattice.copy()
for pair in calc.u[mask][:, :2]:
p1, p2 = sublattice.positions[pair]
traj = np.linspace(p1, p2, 10)
for p in traj[1:-1]:
base.append('B')
base.positions[-1] = p
write('percopath.cif', base)
```
Raw data
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"description": "#### About\n\nPerconeb is the library used for fast identifying of the optimal jumps mobile species required for the percolation in a crystal structure. \n\nThe identified jumps can be used for nudged elastic band (NEB) clalculation of the mobile species activation barriers.\n\nFunctionality includes:\n- 1-3D percolation radius calculations (percolation threshold)\n\n- cutoff search for the shortest ionic jumps between equilibrium positions\n\n- identifying of unique jumps required for 1-3D percolation\n\n- initial trajectory preparation for NEB calculations\n\n\n#### Installation\n!pip install perconeb\n\n#### How to use\n\n\n```python\nfrom core import Perconeb\nfrom ase.io import read\n\nfile = '/Users/artemdembitskiy/Desktop/perconeb/LiCoO2.cif'\natoms = read(file) # can be .cif, POSCAR, or any that is readable by ase\nspecie = 3 # mobile specie, atomic number\ntr = 0.75 # minimum allowed distance between linear segment connecting i,j-th positions of mobile specie\n # and the framework, angstrom\nupper_bound = 10.0 # maximum allowed distance for intersite hops of mobile species, angstrom\n\ncalc = Perconeb(atoms, specie, upper_bound = upper_bound)\ndim, cutoff = calc.percolation_dimensionality(tr = tr)\n\ndimensions = {2: 1, 4: 2, 8: 3} # we use 2x2x2 supercell to evaluate the percolation dimensionality\n # if there is a connection between some point in the unitcell \n # and its closest replica within supercell then we have the percolation\n # number of connected unitcell and dimensionality are related as follows\n # 2 -> 1D, 4 -> 2D, 8 -> 3D\nprint(f'Maximum percolation dimnesionality is {dimensions[dim]}')\nprint(f'That requires mobile specie hops up to {round(cutoff, 3)} Angstorm')\n```\n\n Maximum percolation dimnesionality is 3\n That requires mobile specie hops up to 4.922 Angstorm\n\n\n#### Inequivalent mobile specie hops (edges)\n\n\n```python\nedges, edge_ids, inverse = calc.unique_edges(tr = tr, dim = dim, cutoff = cutoff)\nedges # each edge is represented by a 5D vector [i, j, offset_x, offset_y, offset_z]\nfor edge in edges:\n source, target = edge[:2] # indices of source and target mobile atoms in the mobile sublattice\n offset = edge[2:]\n p_source = atoms[atoms.numbers == specie].positions[source]\n p_target = atoms[atoms.numbers == specie].positions[target]\n distance_vector = p_source - (p_target + np.dot(atoms.cell, offset))\n distance = np.linalg.norm(distance_vector)\n print(f'Source {source}, target {target}, offset {offset}, hop distance {round(distance, 4)}')\n```\n\n Source 0, target 1, offset [0 0 0], hop distance 4.9124\n Source 0, target 0, offset [ 1 -1 0], hop distance 4.7593\n Source 0, target 0, offset [0 1 0], hop distance 2.4346\n\n\n#### Initial gues for NEB\n\n\n```python\nsep_dist = 8.0 # minimum size of the supercell, Angstrom\nidpp = False # whether to use IDPP or linear interpolation scheme\nstep = 1.0 # interpolation step, Angstroms\nout = calc.neb_guess(edges, edge_ids,\n min_sep_dist = sep_dist,\n idpp = idpp,\n step = step\n )\ncalc.write_traj(out, f'{file}_traj.cif') # the whole trajectory can be visualized in jmol\n```\n\n#### Save percolating pathway within supercell\n\n\n```python\nfrom ase.io import write\n\nfile = '/Users/artemdembitskiy/Desktop/perconeb/LiCoO2.cif'\natoms = read(file)\nspecie = 3\n\ncalc = Perconeb(atoms, specie, upper_bound = 10.0)\ndim, cutoff = calc.percolation_dimensionality(tr = 0.75)\nmask = calc._filter_edges(tr = 0.75, cutoff = cutoff)\nsublattice = calc.mobile_supercell\nbase = sublattice.copy()\nfor pair in calc.u[mask][:, :2]:\n p1, p2 = sublattice.positions[pair]\n traj = np.linspace(p1, p2, 10)\n for p in traj[1:-1]:\n base.append('B')\n base.positions[-1] = p\nwrite('percopath.cif', base)\n```\n\n\n",
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