Chimie Théorique » QMX

# Copyright (C) 2010, Jesper Friis

# (see accompanying license files for details).

"""Utility tools for convenient creation of slabs and interfaces of

different orientations."""

import numpy as np

def gcd(seq):

    """Returns greatest common divisor of integers in *seq*."""

    def _gcd(m, n):

        while n:

            m, n = n, m%n

        return m

    return reduce(_gcd, seq)

def get_layers(atoms, miller, tolerance=0.001):

    """Returns two arrays describing which layer each atom belongs

    to and the distance between the layers and origo.

    Parameters:

    miller: 3 integers

        The Miller indices of the planes. Actually, any direction

        in reciprocal space works, so if a and b are two float

        vectors spanning an atomic plane, you can get all layers

        parallel to this with miller=np.cross(a,b).

    tolerance: float

        The maximum distance in Angstrom along the plane normal for

        counting two atoms as belonging to the same plane.

    Returns:

    tags: array of integres

        Array of layer indices for each atom.

    levels: array of floats

        Array of distances in Angstrom from each layer to origo.

    Example:

    >>> import numpy as np

    >>> from ase.lattice.spacegroup import crystal

    >>> atoms = crystal('Al', [(0,0,0)], spacegroup=225, cellpar=4.05)

    >>> np.round(atoms.positions, decimals=5)

    array([[ 0.   ,  0.   ,  0.   ],

           [ 0.   ,  2.025,  2.025],

           [ 2.025,  0.   ,  2.025],

           [ 2.025,  2.025,  0.   ]])

    >>> get_layers(atoms, (0,0,1))

    (array([0, 1, 1, 0]), array([ 0.   ,  2.025]))

    """

    miller = np.asarray(miller)

    metric = np.dot(atoms.cell, atoms.cell.T)

    c = np.linalg.solve(metric.T, miller.T).T

    miller_norm = np.sqrt(np.dot(c, miller))

    d = np.dot(atoms.get_scaled_positions(), miller)/miller_norm

    keys = np.argsort(d)

    ikeys = np.argsort(keys)

    mask = np.concatenate(([True], np.diff(d[keys]) > tolerance))

    tags = np.cumsum(mask)[ikeys]

    if tags.min() == 1:

        tags -= 1

    levels = d[keys][mask]

    return tags, levels

def cut(atoms, a=(1,0,0), b=(0,1,0), c=(0,0,1), origo=(0,0,0),

        nlayers=None, extend=1.0, tolerance=0.001):

    """Cuts out a cell defined by *a*, *b*, *c* and *origo* from a

    sufficiently repeated copy of *atoms*.

    Typically, this function is used to create slabs of different

    sizes and orientations. The vectors *a*, *b* and *c* are in scaled

    coordinates and defines the returned cell and should normally be

    integer-valued in order to end up with a periodic

    structure. However, for systems with sub-translations, like fcc,

    integer multiples of 1/2 or 1/3 might also make sence for some

    directions (and will be treated correctly).

    Parameters:

    atoms: Atoms instance

        This should correspond to a repeatable unit cell.

    a: int | 3 floats

        The a-vector in scaled coordinates of the cell to cut out. If

        integer, the a-vector will be the scaled vector from *origo* to the

        atom with index *a*.

    b: int | 3 floats

        The b-vector in scaled coordinates of the cell to cut out. If

        integer, the b-vector will be the scaled vector from *origo* to the

        atom with index *b*.

    c: int | 3 floats

        The c-vector in scaled coordinates of the cell to cut out. If

        integer, the c-vector will be the scaled vector from *origo* to the

        atom with index *c*. Not used if *nlayers* is given.

    origo: int | 3 floats

        Position of origo of the new cell in scaled coordinates. If

        integer, the position of the atom with index *origo* is used.

    nlayers: int

        If *nlayers* is not *None*, the returned cell will have

        *nlayers* atomic layers in the c-direction. The direction of

        the c-vector will be along cross(a, b) converted to real

        space, i.e. normal to the plane spanned by a and b in

        orthorombic systems.

    extend: 1 or 3 floats

        The *extend* argument scales the effective cell in which atoms

        will be included. It must either be three floats or a single

        float scaling all 3 directions.  By setting to a value just

        above one, e.g. 1.05, it is possible to all the corner and

        edge atoms in the returned cell.  This will of cause make the

        returned cell non-repeatable, but is very usefull for

        visualisation.

    tolerance: float

        Determines what is defined as a plane.  All atoms within

        *tolerance* Angstroms from a given plane will be considered to

        belong to that plane.

    Example:

    >>> import ase

    >>> from ase.lattice.spacegroup import crystal

    >>>

    # Create an aluminium (111) slab with three layers

    #

    # First an unit cell of Al

    >>> a = 4.05

    >>> aluminium = crystal('Al', [(0,0,0)], spacegroup=225,

    ...                     cellpar=[a, a, a, 90, 90, 90])

    >>>

    # Then cut out the slab

    >>> al111 = cut(aluminium, (1,-1,0), (0,1,-1), nlayers=3)

    >>>

    # Visualisation of the skutterudite unit cell

    #

    # Again, create a skutterudite unit cell

    >>> a = 9.04

    >>> skutterudite = crystal(

    ...     ('Co', 'Sb'),

    ...     basis=[(0.25,0.25,0.25), (0.0, 0.335, 0.158)],

    ...     spacegroup=204,

    ...     cellpar=[a, a, a, 90, 90, 90])

    >>>

    # Then use *origo* to put 'Co' at the corners and *extend* to

    # include all corner and edge atoms.

    >>> s = cut(skutterudite, origo=(0.25, 0.25, 0.25), extend=1.01)

    >>> ase.view(s)  # doctest: +SKIP

    """

    atoms = atoms.copy()

    cell = atoms.cell

    if isinstance(origo, int):

        origo = atoms.get_scaled_positions()[origo]

    scaled = (atoms.get_scaled_positions() - origo)%1.0

    scaled %= 1.0 # needed to ensure that all numbers are *less* than one

    atoms.set_scaled_positions(scaled)

    if isinstance(a, int):

        a = scaled[a] - origo

    if isinstance(b, int):

        b = scaled[b] - origo

    if isinstance(c, int):

        c = scaled[c] - origo

    a = np.array(a, dtype=float)

    b = np.array(b, dtype=float)

    origo = np.array(origo, dtype=float)

    if nlayers:

        miller = np.cross(a, b) # surface normal

        # The factor 36 = 2*2*3*3 is because the elements of a and b

        # might be multiples of 1/2 or 1/3 because of lattice

        # subtranslations

        if np.all(36*miller - np.rint(36*miller)) < 1e-5:

            miller = np.rint(36*miller)

            miller /= gcd(miller)

        tags, layers = get_layers(atoms, miller, tolerance)

        while tags.max() < nlayers:

            atoms = atoms.repeat(2)

            tags, layers = get_layers(atoms, miller, tolerance)

        # Convert surface normal in reciprocal space to direction in

        # real space

        metric = np.dot(cell, cell.T)

        c = np.linalg.solve(metric.T, miller.T).T

        c *= layers[nlayers]/np.sqrt(np.dot(c, miller))

        if np.linalg.det(np.dot(np.array([a, b, c]), cell)) < 0:

            c *= -1.0

    newcell = np.dot(np.array([a, b, c]), cell)

    # Create a new atoms object, repeated and translated such that

    # it completely covers the new cell

    scorners_newcell = np.array([[0., 0., 0.], [0., 0., 1.],

                                 [0., 1., 0.], [0., 1., 1.],

                                 [1., 0., 0.], [1., 0., 1.],

                                 [1., 1., 0.], [1., 1., 1.]])

    corners = np.dot(scorners_newcell, newcell*extend)

    scorners = np.linalg.solve(cell.T, corners.T).T

    rep = np.ceil(scorners.ptp(axis=0)).astype('int') + 1

    trans = np.dot(np.floor(scorners.min(axis=0)), cell)

    atoms = atoms.repeat(rep)

    atoms.translate(trans)

    atoms.set_cell(newcell)

    # Mask out atoms outside new cell

    stol = tolerance  # scaled tolerance, XXX

    maskcell = atoms.cell*extend

    sp = np.linalg.solve(maskcell.T, (atoms.positions).T).T

    mask = np.all(np.logical_and(-stol <= sp, sp < 1-stol), axis=1)

    atoms = atoms[mask]

    return atoms

def stack(atoms1, atoms2, axis=2, cell=None, fix=0.5,

          maxstrain=0.5, distance=None):

    """Return a new Atoms instance with *atoms2* added to atoms1

    along the given axis. Periodicity in all directions is

    ensured.

    The size of the final cell is determined by *cell*, except

    that the length alongh *axis* will be the sum of

    *atoms1.cell[axis]* and *atoms2.cell[axis]*. If *cell* is None,

    it will be interpolated between *atoms1* and *atoms2*, where

    *fix* determines their relative weight. Hence, if *fix* equals

    zero, the final cell will be determined purely from *atoms1* and

    if *fix* equals one, it will be determined purely from

    *atoms2*.

    An ValueError exception will be raised if the far corner of

    the unit cell of either *atoms1* or *atoms2* is displaced more

    than *maxstrain*. Setting *maxstrain* to None, disable this

    check.

    If *distance* is provided, the atomic positions in *atoms1* and

    *atoms2* as well as the cell lengths along *axis* will be

    adjusted such that the distance between the distance between

    the closest atoms in *atoms1* and *atoms2* will equal *distance*.

    This option uses scipy.optimize.fmin() and hence require scipy

    to be installed.

    Example:

    >>> import ase

    >>> from ase.lattice.spacegroup import crystal

    >>>

    # Create an Ag(110)-Si(110) interface with three atomic layers

    # on each side.

    >>> a_ag = 4.09

    >>> ag = crystal(['Ag'], basis=[(0,0,0)], spacegroup=225,

    ...              cellpar=[a_ag, a_ag, a_ag, 90., 90., 90.])

    >>> ag110 = cut(ag, (0, 0, 3), (-1.5, 1.5, 0), nlayers=3)

    >>>

    >>> a_si = 5.43

    >>> si = crystal(['Si'], basis=[(0,0,0)], spacegroup=227,

    ...              cellpar=[a_si, a_si, a_si, 90., 90., 90.])

    >>> si110 = cut(si, (0, 0, 2), (-1, 1, 0), nlayers=3)

    >>>

    >>> interface = stack(ag110, si110, maxstrain=1)

    >>> ase.view(interface)  # doctest: +SKIP

    >>>

    # Once more, this time adjusted such that the distance between

    # the closest Ag and Si atoms will be 2.3 Angstrom.

    >>> interface2 = stack(ag110, si110,

    ...                    maxstrain=1, distance=2.3)   # doctest:+ELLIPSIS

    Optimization terminated successfully.

        ...

    >>> ase.view(interface2)  # doctest: +SKIP

    """

    atoms1 = atoms1.copy()

    atoms2 = atoms2.copy()

    c1 = np.linalg.norm(atoms1.cell[axis])

    c2 = np.linalg.norm(atoms2.cell[axis])

    if cell is None:

        cell1 = atoms1.cell.copy()

        cell2 = atoms2.cell.copy()

        cell1[axis] /= c1

        cell2[axis] /= c2

        cell = cell1 + fix*(cell2 - cell1)

    cell[axis] /= np.linalg.norm(cell[axis])

    cell1 = cell.copy()

    cell2 = cell.copy()

    cell1[axis] *= c1

    cell2[axis] *= c2

    if (maxstrain and

        (((cell1 - atoms1.cell).sum(axis=0)**2).sum() > maxstrain**2 or

         ((cell2 - atoms2.cell).sum(axis=0)**2).sum() > maxstrain**2)):

        raise ValueError('Incompatible cells.')

    sp1 = np.linalg.solve(atoms1.cell.T, atoms1.positions.T).T

    sp2 = np.linalg.solve(atoms2.cell.T, atoms2.positions.T).T

    atoms1.set_cell(cell1)

    atoms2.set_cell(cell2)

    atoms1.set_scaled_positions(sp1)

    atoms2.set_scaled_positions(sp2)

    if distance is not None:

        from scipy.optimize import fmin

        def mindist(pos1, pos2):

            n1 = len(pos1)

            n2 = len(pos2)

            idx1 = np.arange(n1).repeat(n2)

            idx2 = np.tile(np.arange(n2), n1)

            return np.sqrt(((pos1[idx1] - pos2[idx2])**2).sum(axis=1).min())

        def func(x):

            t1, t2, h1, h2 = x[0:3], x[3:6], x[6], x[7]

            pos1 = atoms1.positions + t1

            pos2 = atoms2.positions + t2

            d1 = mindist(pos1, pos2 + (h1 + 1.0)*atoms1.cell[axis])

            d2 = mindist(pos2, pos1 + (h2 + 1.0)*atoms2.cell[axis])

            return (d1 - distance)**2 + (d2 - distance)**2

        atoms1.center()

        atoms2.center()

        x0 = np.zeros((8,))

        x = fmin(func, x0)

        t1, t2, h1, h2 = x[0:3], x[3:6], x[6], x[7]

        atoms1.translate(t1)

        atoms2.translate(t2)

        atoms1.cell[axis] *= 1.0 + h1

        atoms2.cell[axis] *= 1.0 + h2

    atoms2.translate(atoms1.cell[axis])

    atoms1.cell[axis] += atoms2.cell[axis]

    atoms1.extend(atoms2)

    return atoms1

#-----------------------------------------------------------------

# Self test

if __name__ == '__main__':

    import doctest

    print 'doctest: ', doctest.testmod()