for i in range(nimages):
images.append(images[0].copy())
images[-1].positions[6, 1] = 2 - images[0].positions[6, 1]
neb = NEB(images)
neb.interpolate()
if 0: # verify that initial images make sense
from ase.visualize import view
view(neb.images)
for image in images:
image.set_calculator(MorsePotential())
dyn = BFGS(neb, trajectory='mep.traj') # , logfile='mep.log')
dyn.run(fmax=fmax)
for a in neb.images:
print(a.positions[-1], a.get_potential_energy())
neb.climb = True
dyn.run(fmax=fmax)
# Check NEB tools.
nt_images = read('mep.traj@-4:')
nebtools = NEBtools(nt_images)
nt_fmax = nebtools.get_fmax(climb=True)
Ef, dE = nebtools.get_barrier()
print(Ef, dE, fmax, nt_fmax)
assert nt_fmax < fmax
assert abs(Ef - 1.389) < 0.001
from ase.neb import NEBtools
from ase.io import read
images = read('neb.traj@-5:')
nebtools = NEBtools(images)
# Get the calculated barrier and the energy change of the reaction.
Ef, dE = nebtools.get_barrier()
# Get the barrier without any interpolation between highest images.
Ef, dE = nebtools.get_barrier(fit=False)
# Get the actual maximum force at this point in the simulation.
max_force = nebtools.get_fmax()
# Create a figure like that coming from ase-gui.
fig = nebtools.plot_band()
fig.savefig('diffusion-barrier.png')
# Create a figure with custom parameters.
from matplotlib import pyplot, rcParams
rcParams.update({'font.size': 10})
fig = pyplot.figure(figsize=(4.5, 3))
ax = fig.add_axes((0.15, 0.15, 0.8, 0.75))
nebtools.plot_band(ax)
fig.savefig('diffusion-barrier.png')
# -*- coding: utf-8 -*-
# creates: diffusion-I.png diffusion-T.png diffusion-F.png diffusion-barrier.png
from ase.io import read, write
from ase.neb import NEBtools
if 1:
exec(compile(open('diffusion1.py').read(), 'diffusion1.py', 'exec'))
exec(compile(open('diffusion2.py').read(), 'diffusion2.py', 'exec'))
exec(compile(open('diffusion4.py').read(), 'diffusion4.py', 'exec'))
exec(compile(open('diffusion5.py').read(), 'diffusion5.py', 'exec'))
images = read('neb.traj@-5:')
for name, a in zip('ITF', images[::2]):
cell = a.get_cell()
del a.constraints
a = a * (2, 2, 1)
a.set_cell(cell)
write('diffusion-%s.pov' % name,
a,
show_unit_cell=True,
transparent=False,
display=False,
run_povray=True)
nebtools = NEBtools(images)
assert abs(nebtools.get_barrier()[0] - 0.374) < 1e-3
nim = len(images)
n = size // nim # number of cpu's per image
j = rank // n # image number
assert nim * n == size
for i in range(nim):
ranks = range(i * n, (i + 1) * n)
if rank in ranks:
calc = getcalc(txt='neb%d.txt' % j, communicator=ranks)
images[i].set_calculator(calc)
autoneb = AutoNEB(attach_calculators,
prefix='neb',
n_simul=2,
parallel=True,
climb=True,
n_max=5,
optimizer='FIRE',
fmax=0.05,
k=0.5,
maxsteps=[25, 1000])
autoneb.run()
nebtools = NEBtools(autoneb.all_images)
barrier, delta_e = nebtools.get_barrier()
print('barrier', barrier)
ref = 0.74051020956857272 # 1.484 <- with better parameters
err = abs(barrier - ref)
assert err < 1e-3, 'barrier={}, expected={}, err={}'.format(barrier, ref, err)
# -*- coding: utf-8 -*-
# creates: diffusion-I.png diffusion-T.png diffusion-F.png diffusion-barrier.png
from ase.io import read, write
from ase.neb import NEBtools
if 1:
exec(compile(open('diffusion1.py').read(), 'diffusion1.py', 'exec'))
exec(compile(open('diffusion2.py').read(), 'diffusion2.py', 'exec'))
exec(compile(open('diffusion4.py').read(), 'diffusion4.py', 'exec'))
exec(compile(open('diffusion5.py').read(), 'diffusion5.py', 'exec'))
images = read('neb.traj@-5:')
for name, a in zip('ITF', images[::2]):
cell = a.get_cell()
del a.constraints
a = a * (2, 2, 1)
a.set_cell(cell)
write('diffusion-%s.pov' % name, a, show_unit_cell=True,
transparent=False, display=False, run_povray=True)
nebtools = NEBtools(images)
assert abs(nebtools.get_barrier()[0] - 0.374) < 1e-3
from ase.neb import NEBtools
from ase.io import read
images = read("neb.traj@-5:")
nebtools = NEBtools(images)
# Get the calculated barrier and the energy change of the reaction.
Ef, dE = nebtools.get_barrier()
# Get the barrier without any interpolation between highest images.
Ef, dE = nebtools.get_barrier(fit=False)
# Get the actual maximum force at this point in the simulation.
max_force = nebtools.get_fmax()
# Create a figure like that coming from ase-gui.
fig = nebtools.plot_band()
fig.savefig("diffusion-barrier.png")
# Create a figure with custom parameters.
from matplotlib import pyplot, rcParams
rcParams.update({"font.size": 10})
fig = pyplot.figure(figsize=(4.5, 3))
ax = fig.add_axes((0.15, 0.15, 0.8, 0.75))
nebtools.plot_band(ax)
fig.savefig("diffusion-barrier.png")
for i in range(nimages):
images.append(images[0].copy())
images[-1].positions[6, 1] = 2 - images[0].positions[6, 1]
neb = NEB(images)
neb.interpolate()
if 0: # verify that initial images make sense
from ase.visualize import view
view(neb.images)
for image in images:
image.set_calculator(MorsePotential())
dyn = BFGS(neb, trajectory='mep.traj') # , logfile='mep.log')
dyn.run(fmax=fmax)
for a in neb.images:
print(a.positions[-1], a.get_potential_energy())
neb.climb = True
dyn.run(fmax=fmax)
# Check NEB tools.
nt_images = read('mep.traj@-4:')
nebtools = NEBtools(nt_images)
nt_fmax = nebtools.get_fmax(climb=True)
Ef, dE = nebtools.get_barrier()
print(Ef, dE, fmax, nt_fmax)
assert nt_fmax < fmax
assert abs(Ef - 1.389) < 0.001
# 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(method='idpp')
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)
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
