# Brillouin zone sampling¶

The k-points are always given relative to the basis vectors of the reciprocal unit cell.

## Monkhorst-Pack¶

ase.dft.kpoints.monkhorst_pack(size)[source]

Construct a uniform sampling of k-space of given size.

The k-points are given as [MonkhorstPack]:

$\sum_{i=1,2,3} \frac{2n_i -N_i - 1}{2N_i} \mathbf{b}_i,$

where $$n_i=1,2,...,N_i$$, size = $$(N_1, N_2, N_3)$$ and the $$\mathbf{b}_i$$’s are reciprocal lattice vectors.

ase.dft.kpoints.get_monkhorst_pack_size_and_offset(kpts)[source]

Find Monkhorst-Pack size and offset.

Returns (size, offset), where:

kpts = monkhorst_pack(size) + offset.


The set of k-points must not have been symmetry reduced.

Example:

>>> from ase.dft.kpoints import *
>>> monkhorst_pack((4, 1, 1))
array([[-0.375,  0.   ,  0.   ],
[-0.125,  0.   ,  0.   ],
[ 0.125,  0.   ,  0.   ],
[ 0.375,  0.   ,  0.   ]])
>>> get_monkhorst_pack_size_and_offset([[0, 0, 0]])
(array([1, 1, 1]), array([ 0.,  0.,  0.]))

MonkhorstPack

Hendrik J. Monkhorst and James D. Pack: Special points for Brillouin-zone integrations, Phys. Rev. B 13, 5188–5192 (1976)

## Special points in the Brillouin zone¶

ase.dft.kpoints.special_points

The below table lists the special points from [Setyawan-Curtarolo].

## Brillouin zone data¶

 CUB (primitive cubic) GXMGRX,MR FCC (face-centred cubic) GXWKGLUWLK,UX BCC (body-centred cubic) GHNGPH,PN TET (primitive tetragonal) GXMGZRAZ,XR,MA BCT1 (body-centred tetragonal) GXMGZPNZ1M,XP BCT2 (body-centred tetragonal) GXYSGZS1NPY1Z,XP ORC (primitive orthorhombic) GXSYGZURTZ,YT,UX,SR ORCF1 (face-centred orthorhombic) GYTZGXA1Y,TX1,XAZ,LG ORCF2 (face-centred orthorhombic) GYCDXGZD1HC,C1Z,XH1,HY,LG ORCF3 (face-centred orthorhombic) GYTZGXA1Y,XAZ,LG ORCI (body-centred orthorhombic) GXLTWRX1ZGYSW,L1Y,Y1Z ORCC (base-centred orthorhombic) GXSRAZGYX1A1TY,ZT HEX (primitive hexagonal) GMKGALHA,LM,KH RHL1 (primitive rhombohedral) GLB1,BZGX,QFP1Z,LP RHL2 (primitive rhombohedral) GPZQGFP1Q1LZ MCL (primitive monoclinic) GYHCEM1AXH1,MDZ,YD MCLC1 (base-centred monoclinic) GYFLI,I1ZF1,YX1,XGN,MG MCLC3 (base-centred monoclinic) GYFHZIF1,H1Y1XGN,MG MCLC5 (base-centred monoclinic) GYFLI,I1ZHF1,H1Y1XGN,MG TRI1a (primitive triclinic) XGY,LGZ,NGM,RG TRI1b (primitive triclinic) XGY,LGZ,NGM,RG TRI2a (primitive triclinic) XGY,LGZ,NGM,RG TRI2b (primitive triclinic) XGY,LGZ,NGM,RG OBL (primitive oblique) GYHCH1XG RECT (primitive rectangular) GXSYGS CRECT (centred rectangular) GXA1YG HEX2D (primitive hexagonal) GMKG SQR (primitive square) MGXM Setyawan-Curtarolo(1,2)

High-throughput electronic band structure calculations: Challenges and tools

Wahyu Setyawan, Stefano Curtarolo

Computational Materials Science, Volume 49, Issue 2, August 2010, Pages 299–312

http://dx.doi.org/10.1016/j.commatsci.2010.05.010

You can find the special points in the Brillouin zone:

>>> from ase.build import bulk
>>> si = bulk('Si', 'diamond', a=5.459)
>>> lat = si.cell.get_bravais_lattice()
>>> print(list(lat.get_special_points()))
['G', 'K', 'L', 'U', 'W', 'X']
>>> path = si.cell.bandpath('GXW', npoints=100)
>>> print(path.kpts.shape)
(100, 3)

ase.dft.kpoints.get_special_points(cell, lattice=None, eps=0.0002)[source]

Return dict of special points.

The definitions are from a paper by Wahyu Setyawana and Stefano Curtarolo:

http://dx.doi.org/10.1016/j.commatsci.2010.05.010

cell: 3x3 ndarray

Unit cell.

lattice: str

Optionally check that the cell is one of the following: cubic, fcc, bcc, orthorhombic, tetragonal, hexagonal or monoclinic.

eps: float

Tolerance for cell-check.

ase.dft.kpoints.bandpath(path, cell, npoints=None, density=None, special_points=None, eps=0.0002)[source]

Make a list of kpoints defining the path between the given points.

path: list or str

Can be:

• a string that parse_path_string() understands: ‘GXL’

• a list of BZ points: [(0, 0, 0), (0.5, 0, 0)]

• or several lists of BZ points if the the path is not continuous.

cell: 3x3

Unit cell of the atoms.

npoints: int

Length of the output kpts list. If too small, at least the beginning and ending point of each path segment will be used. If None (default), it will be calculated using the supplied density or a default one.

density: float

k-points per A⁻¹ on the output kpts list. If npoints is None, the number of k-points in the output list will be: npoints = density * path total length (in Angstroms). If density is None (default), use 5 k-points per A⁻¹. If the calculated npoints value is less than 50, a mimimum value of 50 will be used.

special_points: dict or None

Dictionary mapping names to special points. If None, the special points will be derived from the cell.

eps: float

Precision used to identify Bravais lattice, deducing special points.

You may define npoints or density but not both.

Return a BandPath object.

ase.dft.kpoints.parse_path_string(s)[source]

Parse compact string representation of BZ path.

A path string can have several non-connected sections separated by commas. The return value is a list of sections where each section is a list of labels.

Examples:

>>> parse_path_string('GX')
[['G', 'X']]
>>> parse_path_string('GX,M1A')
[['G', 'X'], ['M1', 'A']]

ase.dft.kpoints.labels_from_kpts(kpts, cell, eps=1e-05, special_points=None)[source]

Get an x-axis to be used when plotting a band structure.

The first of the returned lists can be used as a x-axis when plotting the band structure. The second list can be used as xticks, and the third as xticklabels.

Parameters:

kpts: list

List of scaled k-points.

cell: list

Unit cell of the atomic structure.

Returns:

Three arrays; the first is a list of cumulative distances between k-points, the second is x coordinates of the special points, the third is the special points as strings.

## Band path¶

The BandPath class stores all the relevant band path information in a single object. It is typically created by helper functions such as ase.cell.Cell.bandpath() or ase.lattice.BravaisLattice.bandpath().

class ase.dft.kpoints.BandPath(cell, kpts=None, special_points=None, path=None)[source]

Represents a Brillouin zone path or bandpath.

A band path has a unit cell, a path specification, special points, and interpolated k-points. Band paths are typically created indirectly using the Cell or BravaisLattice classes:

>>> from ase.lattice import CUB
>>> path = CUB(3).bandpath()
>>> path
BandPath(path='GXMGRX,MR', cell=[3x3], special_points={GMRX}, kpts=[40x3])


Band paths support JSON I/O:

>>> from ase.io.jsonio import read_json
>>> path.write('mybandpath.json')
BandPath(path='GXMGRX,MR', cell=[3x3], special_points={GMRX}, kpts=[40x3])

cartesian_kpts()[source]

Get Cartesian kpoints from this bandpath.

free_electron_band_structure(**kwargs)[source]

Return band structure of free electrons for this bandpath.

This is for mostly testing.

get_linear_kpoint_axis(eps=1e-05)[source]

Define x axis suitable for plotting a band structure.

interpolate(path=None, npoints=None, special_points=None, density=None)[source]

Create new bandpath, (re-)interpolating kpoints from this one.

plot(dimension=3, **plotkwargs)[source]

Visualize this bandpath.

Plots the irreducible Brillouin zone and this bandpath.

classmethod read(fd)

Read new instance from JSON file.

transform(op)[source]

Apply 3x3 matrix to this BandPath and return new BandPath.

This is useful for converting the band path to another cell. The operation will typically be a permutation/flipping established by a function such as Niggli reduction.

write(fd)

Write to JSON file.

## Band structure¶

class ase.dft.band_structure.BandStructure(path, energies, reference=0.0)[source]

A band structure consists of an array of eigenvalues and a bandpath.

BandStructure objects support JSON I/O.

get_labels(eps=1e-05)[source]
plot(*args, **kwargs)[source]

Plot this band structure.

classmethod read(fd)

Read new instance from JSON file.

write(fd)

Write to JSON file.

Free electron example:

# creates: cu.png
from ase.build import bulk
from ase.calculators.test import FreeElectrons

a = bulk('Cu')
a.calc = FreeElectrons(nvalence=1,
kpts={'path': 'GXWLGK', 'npoints': 200})
a.get_potential_energy()
bs = a.calc.band_structure()
bs.plot(emax=10, filename='cu.png') ## Interpolation¶

ase.dft.kpoints.monkhorst_pack_interpolate(path, values, icell, bz2ibz, size, offset=(0, 0, 0))[source]

Interpolate values from Monkhorst-Pack sampling.

path: (nk, 3) array-like

Desired path in units of reciprocal lattice vectors.

values: (nibz, …) array-like

Values on Monkhorst-Pack grid.

icell: (3, 3) array-like

Reciprocal lattice vectors.

bz2ibz: (nbz,) array-like of int

Map from nbz points in BZ to nibz reduced points in IBZ.

size: (3,) array-like of int

Size of Monkhorst-Pack grid.

offset: (3,) array-like

Offset of Monkhorst-Pack grid.

Returns values interpolated to path.

## High symmetry paths¶

ase.dft.kpoints.special_paths

The ase.lattice framework provides suggestions for high symmetry paths in the BZ from the [Setyawan-Curtarolo] paper.

>>> from ase.lattice import BCC
>>> lat = BCC(3.5)
>>> lat.get_special_points()
{'G': array([0, 0, 0]), 'H': array([ 0.5, -0.5,  0.5]), 'P': array([0.25, 0.25, 0.25]), 'N': array([0. , 0. , 0.5])}
>>> lat.special_path
'GHNGPH,PN'


In case you want this information without providing the lattice parameter (which is possible for those lattices where the points do not depend on the lattice parameters), the data is also available as dictionaries:

>>> from ase.dft.kpoints(import special_paths, sc_special_points,
...                      parse_path_string)
>>> paths = sc_special_paths['bcc']
>>> paths
[['G', 'H', 'N', 'G', 'P', 'H'], ['P', 'N']]
>>> points = sc_special_points['bcc']
>>> points
{'H': [0.5, -0.5, 0.5], 'N': [0, 0, 0.5], 'P': [0.25, 0.25, 0.25],
'G': [0, 0, 0]}
>>> kpts = [points[k] for k in paths]  # G-H-N-G-P-H
>>> kpts
[[0, 0, 0], [0.5, -0.5, 0.5], [0, 0, 0.5], [0, 0, 0], [0.25, 0.25, 0.25], [0.5, -0.5, 0.5]]


Predefined sets of k-points:

ase.dft.kpoints.cc6_1x1
ase.dft.kpoints.cc12_2x3
ase.dft.kpoints.cc18_sq3xsq3
ase.dft.kpoints.cc18_1x1
ase.dft.kpoints.cc54_sq3xsq3
ase.dft.kpoints.cc54_1x1
ase.dft.kpoints.cc162_sq3xsq3
ase.dft.kpoints.cc162_1x1

Naming convention: cc18_sq3xsq3 is 18 k-points for a sq(3)xsq(3) cell.

Try this:

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from ase.dft.kpoints import cc162_1x1
>>> B = [(1, 0, 0), (-0.5, 3**0.5 / 2, 0), (0, 0, 1)]
>>> k = np.dot(cc162_1x1, B)
>>> plt.plot(k[:, 0], k[:, 1], 'o')  # doctest: +SKIP
[<matplotlib.lines.Line2D object at 0x9b61dcc>]
>>> plt.show() 