def test_diffusion_coefficient():
from ase.md.analysis import DiffusionCoefficient
from ase.atoms import Atoms
from ase.units import fs as fs_conversion
eps = 1e-10
# Creating simple trajectories
# Textbook case. The displacement coefficient should be 0.5 A^2 / fs except for the final molecule
###### He atom
he = Atoms('He', positions=[(0, 0, 0)])
traj_he = [he.copy() for i in range(2)]
traj_he[1].set_positions([(1, 1, 1)])
timestep = 1 * fs_conversion #fs
dc_he = DiffusionCoefficient(traj_he, timestep)
dc_he.calculate(ignore_n_images=0, number_of_segments=1)
ans = dc_he.get_diffusion_coefficients()[0][0]
# Answer in \AA^2/<ASE time unit>
ans_orig = 5.0e-01 / fs_conversion
#dc_he.print_data()
assert(abs(ans - ans_orig) < eps)
###### CO molecule
co = Atoms('CO', positions=[(0, 0, 0), (0, 0, 1)])
traj_co = [co.copy() for i in range(2)]
traj_co[1].set_positions([(-1, -1, -1), (-1, -1, 0)])
dc_co = DiffusionCoefficient(traj_co, timestep, molecule=False)
dc_co.calculate(ignore_n_images=0, number_of_segments=1)
ans = dc_co.get_diffusion_coefficients()[0][0]
#dc_co.print_data()
assert(abs(ans - ans_orig) < eps)
for index in range(2):
dc_co = DiffusionCoefficient(traj_co, timestep, atom_indices=[index], molecule=False)
dc_co.calculate()
ans = dc_co.get_diffusion_coefficients()[0][0]
assert(abs(ans - ans_orig) < eps)
dc_co = DiffusionCoefficient(traj_co, timestep, molecule=True)
dc_co.calculate(ignore_n_images=0, number_of_segments=1)
ans = dc_co.get_diffusion_coefficients()[0][0]
#dc_co.print_data()
assert(abs(ans - ans_orig) < eps)
def test_co_molecule():
co = Atoms('CO', positions=[(0, 0, 0), (0, 0, 1)])
traj_co = [co.copy() for i in range(2)]
traj_co[1].set_positions([(-1, -1, -1), (-1, -1, 0)])
dc_co = DiffusionCoefficient(traj_co, timestep, molecule=False)
dc_co.calculate(ignore_n_images=0, number_of_segments=1)
ans = dc_co.get_diffusion_coefficients()[0][0]
assert (abs(ans - ans_orig) < eps)
for index in range(2):
dc_co = DiffusionCoefficient(traj_co,
timestep,
atom_indices=[index],
molecule=False)
dc_co.calculate()
ans = dc_co.get_diffusion_coefficients()[0][0]
assert (abs(ans - ans_orig) < eps)
dc_co = DiffusionCoefficient(traj_co, timestep, molecule=True)
dc_co.calculate(ignore_n_images=0, number_of_segments=1)
ans = dc_co.get_diffusion_coefficients()[0][0]
assert (abs(ans - ans_orig) < eps)
def _get_jmol_images(
atoms: Atoms,
energies: np.ndarray,
modes: np.ndarray,
ir_intensities: Union[Sequence[float], np.ndarray] = None
) -> Iterator[Atoms]:
"""Get vibrational modes as a series of Atoms with attached data
For each image (Atoms object):
- eigenvalues are attached to image.arrays['mode']
- "mode#" and "frequency_cm-1" are set in image.info
- "IR_intensity" is set if provided in ir_intensities
- "masses" is removed
This is intended to set up the object for JMOL-compatible export using
ase.io.extxyz.
Args:
atoms: The base atoms object; all images have the same positions
energies: Complex vibrational energies in eV
modes: Eigenvectors array corresponding to atoms and energies. This
should cover the full set of atoms (i.e. modes =
vib.get_modes(all_atoms=True)).
ir_intensities: If available, IR intensities can be included in the
header lines. This does not affect the visualisation, but may
be convenient when comparing to experimental data.
Returns:
Iterator of Atoms objects
"""
for i, (energy, mode) in enumerate(zip(energies, modes)):
# write imaginary frequencies as negative numbers
if energy.imag > energy.real:
energy = float(-energy.imag)
else:
energy = energy.real
image = atoms.copy()
image.info.update({
'mode#': str(i),
'frequency_cm-1': energy / units.invcm,
})
image.arrays['mode'] = mode
# Custom masses are quite useful in vibration analysis, but will
# show up in the xyz file unless we remove them
if image.has('masses'):
del image.arrays['masses']
if ir_intensities is not None:
image.info['IR_intensity'] = float(ir_intensities[i])
yield image
def update(self, atoms: Atoms):
if atoms is None and self.atoms is None:
raise RuntimeError("Need an Atoms object to do anything!")
changes = compare_atoms(self.atoms, atoms)
if changes:
self.results = {}
self.atoms = atoms.copy()
if self.need_setup(changes):
self.setup()
def test_atom():
# Creating simple trajectories
# Textbook case. The displacement coefficient should
# be 0.5 A^2 / fs except for the final molecule
# He atom
he = Atoms('He', positions=[(0, 0, 0)])
traj_he = [he.copy() for i in range(2)]
traj_he[1].set_positions([(1, 1, 1)])
dc_he = DiffusionCoefficient(traj_he, timestep)
dc_he.calculate(ignore_n_images=0, number_of_segments=1)
ans = dc_he.get_diffusion_coefficients()[0][0]
assert (abs(ans - ans_orig) < eps)
def __init__(
self,
atoms: Atoms,
hessian: Union[RealSequence4D, np.ndarray],
indices: Union[Sequence[int], np.ndarray] = None,
) -> None:
if indices is None:
self._indices = np.arange(len(atoms), dtype=int)
else:
self._indices = np.array(indices, dtype=int)
n_atoms = self._check_dimensions(atoms,
np.asarray(hessian),
indices=self._indices)
self._atoms = atoms.copy()
self._hessian2d = (np.asarray(hessian).reshape(3 * n_atoms,
3 * n_atoms).copy())
def center_at_origin(atoms: Atoms) -> Atoms:
"""
Returns a copy of the atoms object centered at the lowest atoms in the z axis
:param atoms: Atoms object
:return: Copy of the translated Atoms object
"""
ads = atoms.copy()
if len(ads) == 1:
ads[0].position = (0, 0, 0)
else:
tags, layer_pos = get_layers(ads, (0, 0, 1), 0.3)
if sum(tags == 0) == 1:
center = atoms[0].position
else:
lowest_atoms = ads[tags == 0]
center = np.mean([a.position for a in lowest_atoms], axis=0)
ads.translate(-center)
return ads
positions = np.array(
[
[4.0725, -4.0725, -1.3575],
[1.3575, -1.3575, -1.3575],
[2.715, -2.715, 0.0],
[4.0725, 1.3575, -1.3575],
[0.0, 0.0, 0.0],
[2.715, 2.715, 0.0],
[6.7875, -1.3575, -1.3575],
[5.43, 0.0, 0.0],
]
)
cell = np.array([[5.43, 5.43, 0.0], [5.43, -5.43, 0.0], [0.00, 0.00, 40.0]])
atoms = Atoms(positions=positions, symbols=["Si"] * 8, cell=cell, pbc=[True, True, False])
atoms.translate(np.array([6.1, -0.1, 10.1]))
result_atoms = atoms.copy()
result_atoms.wrap()
correct_pos = np.array(
[
[4.7425, 1.2575, 8.7425],
[7.4575, -1.4575, 8.7425],
[3.385, 2.615, 10.1],
[4.7425, -4.1725, 8.7425],
[6.1, -0.1, 10.1],
[3.385, -2.815, 10.1],
[2.0275, -1.4575, 8.7425],
[0.67, -0.1, 10.1],
]
)
assert np.allclose(correct_pos, result_atoms.get_positions())
from ase.md.analysis import DiffusionCoefficient
from ase.atoms import Atoms
from ase.units import fs as fs_conversion
eps = 1e-10
# Creating simple trajectories
# Textbook case. The displacement coefficient should be 0.5 A^2 / fs except for the final molecule
###### He atom
he = Atoms('He', positions=[(0, 0, 0)])
traj_he = [he.copy() for i in range(2)]
traj_he[1].set_positions([(1, 1, 1)])
timestep = 1 * fs_conversion #fs
dc_he = DiffusionCoefficient(traj_he, timestep)
dc_he.calculate(ignore_n_images=0, number_of_segments=1)
ans = dc_he.get_diffusion_coefficients()[0][0]
# Answer in \AA^2/<ASE time unit>
ans_orig = 5.0e-01 / fs_conversion
#dc_he.print_data()
assert (abs(ans - ans_orig) < eps)
###### CO molecule
co = Atoms('CO', positions=[(0, 0, 0), (0, 0, 1)])
traj_co = [co.copy() for i in range(2)]
traj_co[1].set_positions([(-1, -1, -1), (-1, -1, 0)])
def copy(self):
atoms = AseAtoms.copy(self)
atoms.transfer(self)
atoms.set_celldisp(self.get_celldisp())
return atoms
