print('\nSummary of the results: \n')
atoms_ase = read('neb_ase.traj', ':')
n_eval_ase = int(len(atoms_ase) - 2 * (len(atoms_ase) / n_images))
print('Number of function evaluations CI-NEB implemented in ASE:', n_eval_ase)
# ML-NEB:
atoms_catlearn = read('evaluated_structures.traj', ':')
n_eval_catlearn = len(atoms_catlearn) - 2
print('Number of function evaluations CatLearn:', n_eval_catlearn)
# Comparison:
print(
'\nThe ML-NEB algorithm required ', (n_eval_ase / n_eval_catlearn),
'times less number of function evaluations than '
'the standard NEB algorithm.')
# Plot ASE NEB:
nebtools_ase = NEBTools(images_ase)
Sf_ase = nebtools_ase.get_fit()[2]
Ef_ase = nebtools_ase.get_fit()[3]
Ef_neb_ase, dE_neb_ase = nebtools_ase.get_barrier(fit=False)
nebtools_ase.plot_band()
plt.show()
# Plot ML-NEB predicted path and show images along the path:
plotneb(trajectory='ML-NEB.traj', view_path=False)
import matplotlib.pyplot as plt
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.
fig = plt.figure(figsize=(5.5, 4.0))
ax = fig.add_axes((0.15, 0.15, 0.8, 0.75))
nebtools.plot_band(ax)
fig.savefig('diffusion-barrier.png')
images = [initial]
for i in range(3):
image = initial.copy()
image.set_calculator(EMT())
image.set_constraint(constraint)
images.append(image)
images.append(final)
neb = NEB(images)
neb.interpolate()
qn = BFGS(neb, trajectory='neb.traj')
qn.run(fmax=0.05)
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')
images = [initial]
for index in range(nimages - 2):
images += [initial.copy()]
images[-1].set_calculator(calc())
images += [final]
neb = NEB(images)
neb.interpolate()
dyn = BFGS(neb, trajectory='mep.traj')
dyn.run(fmax=fmax)
# Check climbing image.
neb.climb = True
dyn.run(fmax=fmax)
# Check NEB tools.
nt_images = ase.io.read('mep.traj', index='-{:d}:'.format(nimages))
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
# Plot one band.
nebtools.plot_band()
# Plot many (ok, 2) bands.
nt_images = ase.io.read('mep.traj', index='-{:d}:'.format(2 * nimages))
nebtools = NEBTools(nt_images)
nebtools.plot_bands()
def test_neb(plt):
from ase import Atoms
from ase.constraints import FixAtoms
import ase.io
from ase.neb import NEB, NEBTools
from ase.calculators.morse import MorsePotential
from ase.optimize import BFGS, QuasiNewton
def calc():
# Common calculator for all images.
return MorsePotential()
# Create and relax initial and final states.
initial = Atoms('H7',
positions=[(0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0),
(0, 2, 0), (1, 2, 0), (0.5, 0.5, 1)],
constraint=[FixAtoms(range(6))],
calculator=calc())
dyn = QuasiNewton(initial)
dyn.run(fmax=0.01)
final = initial.copy()
final.calc = calc()
final.positions[6, 1] = 2 - initial.positions[6, 1]
dyn = QuasiNewton(final)
dyn.run(fmax=0.01)
# Run NEB without climbing image.
fmax = 0.05
nimages = 4
images = [initial]
for index in range(nimages - 2):
images += [initial.copy()]
images[-1].calc = calc()
images += [final]
neb = NEB(images)
neb.interpolate()
with BFGS(neb, trajectory='mep.traj') as dyn:
dyn.run(fmax=fmax)
# Check climbing image.
neb.climb = True
dyn.run(fmax=fmax)
# Check NEB tools.
nt_images = ase.io.read('mep.traj', index='-{:d}:'.format(nimages))
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
# Plot one band.
nebtools.plot_band()
# Plot many (ok, 2) bands.
nt_images = ase.io.read('mep.traj', index='-{:d}:'.format(2 * nimages))
nebtools = NEBTools(nt_images)
nebtools.plot_bands()
