CIF¶

The Cif class generates an object for each .cif file. Each object can

• compute the coordination numbers for each site label using four methods
• compute bond fractions and bond counts at each site within the specified cutoff radius or based on -coordination number geometry
• categorize atomic mixing at bond-pair and file levels
• list all possible bond and label pairs derived from the formula
• generate a unit cell and a supercell by applying ±1 shifts
• parse formulas, structures, tags, space group names, supercell sizes, and elements

Google Codelab¶

You can also run the examples using Google Codelab by clicking the link below:

You can initialize Cif object using a file path to the .cif file. Or you can simply use the example .cif provided in cifkit below.

In cifkit we provide .cif files that can be accessed through from cifkit import Example as shown below. For advancuser, these example .cif files are located under src/cifkit/data in the package.

In [ ]:

from cifkit import Example
from cifkit import Cif

# Initialize with the example file provided
cif = Cif(Example.Er10Co9In20_file_path)

# Print attributes
print("File name:", cif.file_name)
print("Formula:", cif.formula)
print("Unique element:", cif.unique_elements)

from cifkit import Example from cifkit import Cif # Initialize with the example file provided cif = Cif(Example.Er10Co9In20_file_path) # Print attributes print("File name:", cif.file_name) print("Formula:", cif.formula) print("Unique element:", cif.unique_elements)

Get instant properties - parsed information¶

The Cif class provides a set of accessible properties that can be accessed. Each object is initialized with the file_path to the .cif file.

In [ ]:

from cifkit import Cif, Example
import json

# Utility function for printing dictionary
def prettify_dict(dict_data):
  return json.dumps(dict_data, indent=4)

# Initialize
cif = Cif(Example.ErCoIn5_file_path)

# Print instantly available properties
print("Formula:", cif.formula)
print("Structure:", cif.structure)
print("Unique elements:", cif.unique_elements)
print("Unitcell lengths:", cif.unitcell_lengths)
print("Unitcell angles:", cif.unitcell_angles)
print("Site labels:", cif.site_labels)
print("Weight:", cif.weight)
print("Atomic mixing type:", cif.site_mixing_type)
print("Space group number:", cif.space_group_number)
print("Space group number:", cif.space_group_name)
print("Composition type:", cif.composition_type) # 3 -> Ternary
print("Tag:", cif.tag)
print("Atom_site_info:", prettify_dict(cif.atom_site_info))
print("Wyckoff_symbol of Er1:", cif.atom_site_info["Er"]["wyckoff_symbol"])

from cifkit import Cif, Example import json # Utility function for printing dictionary def prettify_dict(dict_data): return json.dumps(dict_data, indent=4) # Initialize cif = Cif(Example.ErCoIn5_file_path) # Print instantly available properties print("Formula:", cif.formula) print("Structure:", cif.structure) print("Unique elements:", cif.unique_elements) print("Unitcell lengths:", cif.unitcell_lengths) print("Unitcell angles:", cif.unitcell_angles) print("Site labels:", cif.site_labels) print("Weight:", cif.weight) print("Atomic mixing type:", cif.site_mixing_type) print("Space group number:", cif.space_group_number) print("Space group number:", cif.space_group_name) print("Composition type:", cif.composition_type) # 3 -> Ternary print("Tag:", cif.tag) print("Atom_site_info:", prettify_dict(cif.atom_site_info)) print("Wyckoff_symbol of Er1:", cif.atom_site_info["Er"]["wyckoff_symbol"])

How tag is parsed¶

Tag is parsed from the third line of each .cif file. Some databases such as Pearson's Crystal Data offers tags for each file.

Get instant properties - bond pairs¶

• The following code returns all possible element and site pairs from the formula in the .cif file.
• The mixing_info_per_label_pair and mixing_info_per_label_pair_sorted_by_mendeleev return site mixing information at the pair level.

In [ ]:

from cifkit import Cif, Example

# Initialize
cif = Cif(Example.ErCoIn5_file_path)

# Utility function for printing a set of tuples
def print_tuples(tuples):
  for pair in tuples:
    print(f"({pair[0]}, {pair[1]})")

# All bond pairs
print("\nAll possible bond pairs:")
print_tuples(cif.bond_pairs)

print("\nAll possible bond pairs sorted by Mendeleev:")
print_tuples(cif.bond_pairs_sorted_by_mendeleev)

# All label pairs
print("\nAll possible label pairs from the formula:")
print_tuples(cif.site_label_pairs)

print("\nAll possible label pairs sorted by Mendeleev:")
print_tuples(cif.site_label_pairs_sorted_by_mendeleev)

# Atomic mixing per pair
print("\nAtomic mixing per label pair:")
print((cif.mixing_info_per_label_pair))

print("\nAtomic mixing per label pair sorted by Mendeleev:")
print(cif.mixing_info_per_label_pair_sorted_by_mendeleev)

from cifkit import Cif, Example # Initialize cif = Cif(Example.ErCoIn5_file_path) # Utility function for printing a set of tuples def print_tuples(tuples): for pair in tuples: print(f"({pair[0]}, {pair[1]})") # All bond pairs print("\nAll possible bond pairs:") print_tuples(cif.bond_pairs) print("\nAll possible bond pairs sorted by Mendeleev:") print_tuples(cif.bond_pairs_sorted_by_mendeleev) # All label pairs print("\nAll possible label pairs from the formula:") print_tuples(cif.site_label_pairs) print("\nAll possible label pairs sorted by Mendeleev:") print_tuples(cif.site_label_pairs_sorted_by_mendeleev) # Atomic mixing per pair print("\nAtomic mixing per label pair:") print((cif.mixing_info_per_label_pair)) print("\nAtomic mixing per label pair sorted by Mendeleev:") print(cif.mixing_info_per_label_pair_sorted_by_mendeleev)

How atomic mixing type is defined¶

Each bonding pair or each file is defined with one of four atomic mixing categories:

• Full occupancy is assigned when a single atomic site occupies the fractional coordinate with an occupancy value of 1.
• Full occupancy with atomic mixing is assigned when multiple atomic sites collectively occupy the fractional coordinate to a sum of 1.
• Deficiency without atomic mixing is assigned when a single atomic site occupying the fractional coordinate with a sum less than 1.
• Deficiency with atomic mixing is assigned when multiple atomic sites occupy the fractional coordinate with a sum less than 1.

Get computed properties - nearest connections per site¶

This section involves computing distances between atoms. Unlike instant properties, these properties may require extensive computation, typically ranging from 1-2 seconds for larger supercells containing more than 3,000 atoms.

These properties are loaded lazily, meaning accessing any of the properties will execute compute_connections() internally. This function will then compute connections, providing all the nearest neighbors for each site.

There are options in the Cif class to use either the by_d_min_method or by_best_methods. Please refer to the README.md for complete documentation.

In [ ]:

from cifkit import Cif, Example

def print_connected_points(all_labels_connections):
    """
    Utility function for printing connections per site label
    """
    for label, connections in all_labels_connections.items():
        print(f"\nAtom site {label}:")
        for (
            connected_label,
            dist,
            coords_1,
            coords_2,
        ) in connections:
            print(f"{connected_label} {dist} {coords_1}, {coords_2}")


# Initialize
cif = Cif(Example.ErCoIn5_file_path)

# Print CN Connections
print("\nFind CN_connections_by_min_dist_method:")
print_connected_points(cif.CN_connections_by_min_dist_method)

print("\nFind CN_connections_by_best_methods:")
print_connected_points(cif.CN_connections_by_best_methods)

from cifkit import Cif, Example def print_connected_points(all_labels_connections): """ Utility function for printing connections per site label """ for label, connections in all_labels_connections.items(): print(f"\nAtom site {label}:") for ( connected_label, dist, coords_1, coords_2, ) in connections: print(f"{connected_label} {dist} {coords_1}, {coords_2}") # Initialize cif = Cif(Example.ErCoIn5_file_path) # Print CN Connections print("\nFind CN_connections_by_min_dist_method:") print_connected_points(cif.CN_connections_by_min_dist_method) print("\nFind CN_connections_by_best_methods:") print_connected_points(cif.CN_connections_by_best_methods)

Get computed properties - distances¶

You can get the shortest distance from each site label or the shortest distance for each possible bond pair.

In [ ]:

from cifkit import Cif, Example

# Initialize
cif = Cif(Example.ErCoIn5_file_path)

print("Shortest distance:", cif.shortest_distance)
print("Shortest bond pair distances:", cif.shortest_bond_pair_distance)
print("Shortest site pair distances:", cif.shortest_site_pair_distance)

from cifkit import Cif, Example # Initialize cif = Cif(Example.ErCoIn5_file_path) print("Shortest distance:", cif.shortest_distance) print("Shortest bond pair distances:", cif.shortest_bond_pair_distance) print("Shortest site pair distances:", cif.shortest_site_pair_distance)

Get computed properties - coordination numbers¶

Compute avg, min, max, unique of coordination numbers determined by one of the best methods or min distance method.

In [ ]:

from cifkit import Cif, Example

# Initialize
cif = Cif(Example.ErCoIn5_file_path)

# Bond counts
print("\nCN_bond_count_by_min_dist_method:")
print(cif.CN_bond_count_by_min_dist_method)

print("\nCN_bond_count_by_min_dist_method_sorted_by_mendeleev:")
print(cif.CN_bond_count_by_min_dist_method_sorted_by_mendeleev)

print("\nCN_bond_count_by_best_methods:")
print(cif.CN_bond_count_by_best_methods)

print("\nCN_bond_count_by_best_methods_sorted_by_mendeleev:")
print(cif.CN_bond_count_by_best_methods_sorted_by_mendeleev)

# Bond fractions
print("\nCN_bond_fractions_by_min_dist_method:")
print(cif.CN_bond_fractions_by_min_dist_method)

print("\nCN_bond_fractions_by_min_dist_method_sorted_by_mendeleev:")
print(cif.CN_bond_fractions_by_min_dist_method_sorted_by_mendeleev)

print("\nCN_bond_fractions_by_best_methods:")
print(cif.CN_bond_fractions_by_best_methods)

print("\nCN_bond_fractions_by_best_methods_sorted_by_mendeleev:")
print(cif.CN_bond_fractions_by_best_methods_sorted_by_mendeleev)

# Unique coordination numbers
print("\nCN_unique_values_by_min_dist_method")
print(cif.CN_unique_values_by_min_dist_method)

print("\nCN_unique_values_by_best_methods")
print(cif.CN_unique_values_by_best_methods)

# Average coordination number
print("\nCN_avg_by_min_dist_method:")
print(cif.CN_avg_by_min_dist_method)

print("\nCN_avg_by_best_methods: ")
print(cif.CN_avg_by_best_methods)

# Min coordination number
print("\nCN_max_by_min_dist_method:")
print(cif.CN_max_by_min_dist_method)

print("\nCN_max_by_best_methods:")
print(cif.CN_max_by_best_methods)

# Max coordination number
print("\nCN_min_by_min_dist_method:")
print(cif.CN_min_by_min_dist_method)

print("\nCN_min_by_best_methods:")
print(cif.CN_min_by_best_methods)

from cifkit import Cif, Example # Initialize cif = Cif(Example.ErCoIn5_file_path) # Bond counts print("\nCN_bond_count_by_min_dist_method:") print(cif.CN_bond_count_by_min_dist_method) print("\nCN_bond_count_by_min_dist_method_sorted_by_mendeleev:") print(cif.CN_bond_count_by_min_dist_method_sorted_by_mendeleev) print("\nCN_bond_count_by_best_methods:") print(cif.CN_bond_count_by_best_methods) print("\nCN_bond_count_by_best_methods_sorted_by_mendeleev:") print(cif.CN_bond_count_by_best_methods_sorted_by_mendeleev) # Bond fractions print("\nCN_bond_fractions_by_min_dist_method:") print(cif.CN_bond_fractions_by_min_dist_method) print("\nCN_bond_fractions_by_min_dist_method_sorted_by_mendeleev:") print(cif.CN_bond_fractions_by_min_dist_method_sorted_by_mendeleev) print("\nCN_bond_fractions_by_best_methods:") print(cif.CN_bond_fractions_by_best_methods) print("\nCN_bond_fractions_by_best_methods_sorted_by_mendeleev:") print(cif.CN_bond_fractions_by_best_methods_sorted_by_mendeleev) # Unique coordination numbers print("\nCN_unique_values_by_min_dist_method") print(cif.CN_unique_values_by_min_dist_method) print("\nCN_unique_values_by_best_methods") print(cif.CN_unique_values_by_best_methods) # Average coordination number print("\nCN_avg_by_min_dist_method:") print(cif.CN_avg_by_min_dist_method) print("\nCN_avg_by_best_methods: ") print(cif.CN_avg_by_best_methods) # Min coordination number print("\nCN_max_by_min_dist_method:") print(cif.CN_max_by_min_dist_method) print("\nCN_max_by_best_methods:") print(cif.CN_max_by_best_methods) # Max coordination number print("\nCN_min_by_min_dist_method:") print(cif.CN_min_by_min_dist_method) print("\nCN_min_by_best_methods:") print(cif.CN_min_by_best_methods)

Draw polyhedrons¶

You may use Jupyter notebook or a python script to execute the following to generate and save a polyhedron generated from each site and the nearest neighbor atoms are determined from the coordination number geometry.

In [ ]:

from cifkit import Cif, Example
import warnings
warnings.filterwarnings('ignore') # For Jupyter Notebook only

# Initialize
cif = Cif(Example.ErCoIn5_file_path)

# Enter site labels. you can get site labels using cif.labels
print("Site labels:", cif.site_labels)

# Plot
cif.plot_polyhedron("In1", is_displayed=True)

from cifkit import Cif, Example import warnings warnings.filterwarnings('ignore') # For Jupyter Notebook only # Initialize cif = Cif(Example.ErCoIn5_file_path) # Enter site labels. you can get site labels using cif.labels print("Site labels:", cif.site_labels) # Plot cif.plot_polyhedron("In1", is_displayed=True)