def test_idpp():
initial = molecule('C2H6')
final = initial.copy()
final.positions[2:5] = initial.positions[[3, 4, 2]]
images = [initial]
for i in range(5):
images.append(initial.copy())
images.append(final)
neb = NEB(images)
d0 = images[3].get_distance(2, 3)
neb.interpolate()
d1 = images[3].get_distance(2, 3)
idpp_interpolate(neb, fmax=0.005)
d2 = images[3].get_distance(2, 3)
print(d0, d1, d2)
assert abs(d2 - 1.74) < 0.01
def test_neb_tr():
nimages = 3
fmax = 0.01
for remove_rotation_and_translation in [True, False]:
# Define coordinates for initial and final states
initial = Atoms('O4', [(1.94366484, 2.24788196, 2.32204726),
(3.05353823, 2.08091038, 2.30712548),
(2.63770601, 3.05694348, 2.67368242),
(2.50579418, 2.12540646, 3.28585811)])
final = Atoms('O4', [(1.95501370, 2.22270649, 2.33191017),
(3.07439495, 2.13662682, 2.31948449),
(2.44730550, 1.26930465, 2.65964947),
(2.52788189, 2.18990240, 3.29728667)])
final.set_cell((5, 5, 5))
initial.set_cell((5, 5, 5))
final.calc = LennardJones()
initial.calc = LennardJones()
images = [initial]
# Set calculator
for i in range(nimages):
image = initial.copy()
image.calc = LennardJones()
images.append(image)
images.append(final)
# Define the NEB and make a linear interpolation
# with removing translational
# and rotational degrees of freedom
neb = NEB(
images,
remove_rotation_and_translation=remove_rotation_and_translation)
neb.interpolate()
# Test used these old defaults which are not optimial, but work
# in this particular system
idpp_interpolate(neb, fmax=0.1, optimizer=BFGS)
qn = FIRE(neb, dt=0.005, maxstep=0.05, dtmax=0.1)
qn.run(steps=20)
# Switch to CI-NEB, still removing the external degrees of freedom
# Also spesify the linearly varying spring constants
neb = NEB(
images,
climb=True,
remove_rotation_and_translation=remove_rotation_and_translation)
qn = FIRE(neb, dt=0.005, maxstep=0.05, dtmax=0.1)
qn.run(fmax=fmax)
images = neb.images
nebtools = NEBTools(images)
Ef_neb, dE_neb = nebtools.get_barrier(fit=False)
nsteps_neb = qn.nsteps
if remove_rotation_and_translation:
Ef_neb_0 = Ef_neb
nsteps_neb_0 = nsteps_neb
assert abs(Ef_neb - Ef_neb_0) < 1e-2
assert nsteps_neb_0 < nsteps_neb * 0.7
for i in range(nimages):
image = initial.copy()
image.set_calculator(LennardJones())
images.append(image)
images.append(final)
# Define the NEB and make a linear interpolation
# with removing translational
# and rotational degrees of freedom
neb = NEB(images,
remove_rotation_and_translation=remove_rotation_and_translation)
neb.interpolate()
# Test used these old defaults which are not optimial, but work
# in this particular system
idpp_interpolate(neb, fmax=0.1, optimizer=BFGS)
qn = FIRE(neb, dt=0.005, maxmove=0.05, dtmax=0.1)
qn.run(steps=20)
# Switch to CI-NEB, still removing the external degrees of freedom
# Also spesify the linearly varying spring constants
neb = NEB(images,
climb=True,
remove_rotation_and_translation=remove_rotation_and_translation)
qn = FIRE(neb, dt=0.005, maxmove=0.05, dtmax=0.1)
qn.run(fmax=fmax)
images = neb.images
nebtools = NEBTools(images)
from ase.build import molecule
from ase.neb import NEB, idpp_interpolate
initial = molecule('C2H6')
final = initial.copy()
final.positions[2:5] = initial.positions[[3, 4, 2]]
images = [initial]
for i in range(5):
images.append(initial.copy())
images.append(final)
neb = NEB(images)
d0 = images[3].get_distance(2, 3)
neb.interpolate()
d1 = images[3].get_distance(2, 3)
idpp_interpolate(neb, fmax=0.005)
d2 = images[3].get_distance(2, 3)
print(d0, d1, d2)
assert abs(d2 - 1.74) < 0.01
