def initialize_CM(n_images=8,
nest=1,
write_POSCAR=True,
write_python=True,
python_template='template.py'):
"""
Initializes the NEB calculation.
Parameters supported:
n_images: Number of intermittent images in folders ## where # is a number. Default is 6.
nest: Indicates how nested the trial is in the constrained minimization folder. Default is 1.
write_POSCAR: If True, it will write a POSCAR file to the folders as POSCAR_start. Default is True.
write_python: If True, it will copy the template file to the folders. Default is True.
"""
file_base = relpath('.', '../' * nest).replace('/', '_')
#Read initial and final state
initial = read('./initial/CONTCAR')
final = read('./final/CONTCAR')
#Generating the interpolated images
images = [initial]
for i in range(n_images):
images.append(initial.copy())
images.append(final)
interpolate(images)
#for i in range(1, n_images+1):
# print 'Checking bond lengths for image %d' % i
# check_bond_lengths(images[i], False)
compare_initial_final(initial, final)
for i in range(0, n_images + 2):
print(('Processing image %d' % i))
folder = label_folder(i)
if not exists(folder):
makedirs(folder)
if write_POSCAR:
POSCAR_path = join(folder, 'POSCAR_start')
print(('Writing image %d to %s' % (i, POSCAR_path)))
write(POSCAR_path, images[i])
if write_python:
python_path = join(folder, '{}{}.py'.format(file_base, folder))
print(('Copying template.py to %s' % python_path))
sh.copyfile(python_template, python_path)
print('Completed initialize_NEB')
def get_neb_trajectory(endpoint1, endpoint2=None, nimages=5):
"""Return a NEB trajectory with IDPP interpolation.
Parameters
----------
endpoint1 : Atoms object | list | str
The second endpoint for use in the NEB. Can be either an
atoms object, a path to an atoms object. Alternatively, it
may be a list or 2 paths or atoms objects if endpoint2 is None.
endpoint2 : Atoms object | str
The second endpoint for use in the NEB. Can be either an
atoms object or a path to an atoms object.
nimages : int
Number of images to include in the trajectory.
Returns
-------
images : list of Atoms objects (N,)
Trajectory of the provided endpoints.
"""
if endpoint2 is None:
trajectory = endpoint1
else:
trajectory = [endpoint1, endpoint2]
for i, image in enumerate(trajectory):
if isinstance(image, str):
trajectory[i] = ase.io.read(image)
images = [trajectory[0]]
for i in range(nimages - 2):
images += [trajectory[0].copy()]
images += [trajectory[-1]]
neb = ase.neb.NEB(images)
neb.interpolate(method='idpp')
return images
def test_interpolate_images_fixed(images, initial, average_pos):
for image in images:
image.set_constraint(FixAtoms([0]))
# test raising a RuntimeError here
with pytest.raises(RuntimeError, match=r"Constraint\(s\) in image number"):
interpolate(images)
interpolate(images, apply_constraint=True)
assert images[1].positions == pytest.approx(images[0].positions)
assert np.allclose(images[1].cell, initial.cell)
interpolate(images, apply_constraint=False)
assert images[1].positions == pytest.approx(average_pos)
assert_interpolated([image.positions for image in images])
assert np.allclose(images[1].cell, initial.cell)
NIMAGES = 9
print(("Restart = %r" % restart))
print(("NIMAGES = %d" % NIMAGES))
#Read initial and final state
initial = read('../In2O3_110_H_24/In2O3_110_H_24.traj')
final_path = '../In2O3_110_H_76/In2O3_110_H_76.traj'
final = read(final_path)
#Generating the interpolated images
images = [initial]
for i in range(NIMAGES):
images.append(initial.copy())
images.append(final)
interpolate(images)
for i in range(0, (NIMAGES + 2)):
print(('Processing image %d' % i))
dir = label_folder(i)
if not os.path.exists(dir):
os.makedirs(dir)
if restart:
if i != 0 and i != (NIMAGES + 1):
print("Restarting calculation. Copying CONTCAR to POSCAR")
image = read(dir + '/CONTCAR')
else:
image = read(dir + '/POSCAR')
else:
print("Starting calculation. Writing POSCAR from interpolation")
image = images[i]
def test_interpolate_images_cell_default_interpolate_cell_scaled_coord(
images, initial):
interpolate(images, interpolate_cell=True, use_scaled_coord=True)
assert_interpolated([image.get_scaled_positions() for image in images])
assert_interpolated([image.cell for image in images])
def test_interpolate_images_cell(images, initial, average_pos):
interpolate(images, interpolate_cell=True)
assert images[1].positions == pytest.approx(average_pos)
assert_interpolated([image.positions for image in images])
assert_interpolated([image.cell for image in images])
def test_interpolate_images_scaled_coord(images, initial):
interpolate(images, use_scaled_coord=True)
assert_interpolated([image.get_scaled_positions() for image in images])
assert np.allclose(images[1].cell, initial.cell)
def test_interpolate_images_default(images, initial, average_pos):
interpolate(images)
assert images[1].positions == pytest.approx(average_pos)
assert_interpolated([image.positions for image in images])
assert np.allclose(images[1].cell, initial.cell)