Dimensionality analysis¶

This is a example of analysis of the dimensionality of a structure using the ase.geometry.dimensionality.analyze_dimensionality() function. This is useful for finding low-dimensional materials, such as 1D chain-like structures, 2D layered structures, or structures with multiple dimensionality types, such as 1D+3D.

The example below creates a layered MoS₂ structure and analyzes its dimensionality.

import ase.build
from ase.visualize import view
from ase.geometry.dimensionality import analyze_dimensionality


atoms = ase.build.mx2(formula='MoS2', kind='2H', a=3.18, thickness=3.19)
atoms.cell[2, 2] = 7.0
atoms.set_pbc((1, 1, 1))
atoms *= 3

intervals = analyze_dimensionality(atoms, method='RDA')
m = intervals[0]
print(sum([e.score for e in intervals]))
print(m.dimtype, m.h, m.score, m.a, m.b)

atoms.set_tags(m.components)
view(atoms)

Coloring the atoms by their tags shows the distinct bonded clusters, which in this case are separate layers.

Each component in the material can be extracted, or “isolated”, using the ase.geometry.dimensionality.isolate_components() function as the example below demonstrates.

import numpy as np
import ase.build
from ase import Atoms
from ase.visualize import view
from ase.geometry.dimensionality import isolate_components


# build two slabs of different types of MoS2
rep = [4, 4, 1]
a = ase.build.mx2(formula='MoS2', kind='2H', a=3.18, thickness=3.19) * rep
b = ase.build.mx2(formula='MoS2', kind='1T', a=3.18, thickness=3.19) * rep
positions = np.concatenate([a.get_positions(), b.get_positions() + [0, 0, 7]])
numbers = np.concatenate([a.numbers, b.numbers])
cell = a.cell
atoms = Atoms(numbers=numbers, positions=positions, cell=cell, pbc=[1, 1, 1])
atoms.cell[2, 2] = 14.0


# isolate each component in the whole material
result = isolate_components(atoms)
print("counts:", [(k, len(v)) for k, v in sorted(result.items())])

for dim, components in result.items():
    for atoms in components:
        print(dim)
        view(atoms, block=True)

The method is described in the article:

P.M. Larsen, M. Pandey, M. Strange, and K. W. Jacobsen

Definition of a scoring parameter to identify low-dimensional materials components

Phys. Rev. Materials 3 034003, 2019

A preprint is available here.