Finding defects in a POSCAR file

In this example, we’ll demonstrate how to automatically find defects in a POSCAR file

Let’s get started!

import os
from itertools import product
import pyprocar.pyposcar as p
import numpy as np
import pyvista as pv
from pyprocar.utils import ROOT

data_dir=os.path.join(ROOT,'data','examples','PyPoscar','03-defects')
# You do not need this. This is to ensure an image is rendered off screen when generating exmaple gallery.
pv.OFF_SCREEN = True

Utility function for creating GIF visualizations

def create_gif(atoms, labels, unit_cell, save_file):
    plotter = pv.Plotter()
    title = save_file.split(os.sep)[-1].split('.')[0]
    plotter.add_title(title)
    plotter.add_mesh(unit_cell.delaunay_3d().extract_feature_edges(), color='black', line_width=5, render_lines_as_tubes=True)
    plotter.add_point_labels(points=atoms.points, labels=labels, show_points=False, always_visible=True)
    plotter.add_mesh(atoms, scalars='atoms', point_size=30, render_points_as_spheres=True, show_scalar_bar=False)
    path = plotter.generate_orbital_path(n_points=36)
    plotter.open_gif(os.path.join(data_dir, save_file))
    plotter.orbit_on_path(path, write_frames=True, viewup=[0, 0, 1], step=0.05)
    plotter.close()

Finding defects

print('Loading an AGNR with a defect in it')
a = p.poscar.Poscar(os.path.join(data_dir,"POSCAR-AGNR-defect.vasp"), verbose=False)
a.parse()

defects = p.defects.FindDefect(a)
print('The defects are:')
print(defects.all_defects)

print('\n\nThe warning above indicates that there are two different types of defects')

print('saving a file with the defects, defects.vasp')
defects.write_defects(filename=os.path.join(data_dir,'defects.vasp'))
print('Inspection of the file defects.vasp shows that the first type of defects are subtitutionals (0D), and the second are the AGRN edges (1D)')


tmp_a = p.Poscar(os.path.join(data_dir, "POSCAR-AGNR-defect.vasp"))
tmp_a.parse()

# Convert positions to Cartesian coordinates for visualization
atoms_before = pv.PolyData(np.dot(tmp_a.dpos, tmp_a.lat))
atoms_before['atoms'] = tmp_a.elm
labels_before = [elm for elm, point in zip(tmp_a.elm, tmp_a.dpos)]
# Define the unit cell using lattice vectors
unit_cell_comb = list(product([0, 1], repeat=3))
unit_cell = np.array([comb[0]*tmp_a.lat[0] + comb[1]*tmp_a.lat[1] + comb[2]*tmp_a.lat[2] for comb in unit_cell_comb])
unit_cell_before = pv.PolyData(unit_cell)

tmp_a = p.Poscar(os.path.join(data_dir, "defects.vasp"))
tmp_a.parse()

# Convert positions to Cartesian coordinates for visualization
atoms_after = pv.PolyData(np.dot(tmp_a.dpos, tmp_a.lat))
atoms_after['atoms'] = tmp_a.elm
labels_after = [elm for elm, point in zip(tmp_a.elm, tmp_a.dpos)]
# Define the unit cell using lattice vectors
unit_cell_comb = list(product([0, 1], repeat=3))
unit_cell = np.array([comb[0]*tmp_a.lat[0] + comb[1]*tmp_a.lat[1] + comb[2]*tmp_a.lat[2] for comb in unit_cell_comb])
unit_cell_after = pv.PolyData(unit_cell)

create_gif(atoms=atoms_before, labels=labels_before, unit_cell=unit_cell_before, save_file='atoms_before_defect_finding.gif')
create_gif(atoms=atoms_after, labels=labels_after, unit_cell=unit_cell_after, save_file='atoms_after_defect_finding.gif')

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Loading an AGNR with a defect in it


WARNING: in FindDefect.find_forgein_atoms() more than two sets of atoms were found. Cluster delimited by `minima`=  [12 29] , `maxima=` [ 7 15 42]
Only elements with less than  12 atoms are regarded as defects


WARNING: in FindDefect.nearest_neighbors_environment() more than two sets of atoms were found. Cluster delimited by `minima`=  [15 30] , `maxima=` [10 19 40]
Only elements with environments less abundant than  15  are regarded as defects
The defects are:
[1, 2, 16, 17, 19, 20, 22, 25, 26, 27, 32, 33, 35, 36, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]


The warning above indicates that there are two different types of defects
saving a file with the defects, defects.vasp
Inspection of the file defects.vasp shows that the first type of defects are subtitutionals (0D), and the second are the AGRN edges (1D)