def get_data(name):
assert name in ("Co", "Zn")
traj = Trajectory(traj_name(name))
nb = NEBTools(traj)
s, E, *_ = nb.get_fit()
E[-1] = E[0] # force energy to be same
ss = numpy.linspace(min(s), max(s))
ee = interp1d(s, E, kind="cubic")(ss)
print(max(ee))
return s, E, ss, ee
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)
neb = NEB(images, climb=True, k=0.6)
nebTools = NEBTools(images)
neb.interpolate('idpp')
print(" -> start neb run")
opt = FIRE(neb)
opt.run(fmax=0.05)
print(nebTools.get_barrier())
# get IRC data
Ef, dE = nebTools.get_barrier()
max_force = nebTools.get_fmax()
x, y, x_fit, y_fit, forces = nebTools.get_fit()
# save IRC data
np.save("x_claisen_mp2.npy", x)
np.save("y_claisen_mp2.npy", y)
np.save("x_claisen_fit_mp2.npy", x_fit)
np.save("y_claisen_fit_mp2.npy", y_fit)
# write NEB guess of interpolation
for i, image in enumerate(images):
out_name = "claisen_{:03d}.xyz".format(i)
io.write(out_name, image)
# plot IRC
y2 *= 23.06
y_fit *= 23.06
def plotneb(trajectory='ML_NEB_catlearn.traj', view_path=True):
"""
Plot NEB path from a trajectory file containing the optimized images.
This is meant to be used with ML-NEB.
The error bars show the uncertainty for each image along the path.
"""
images = read(trajectory, ':')
# Get fit from NEBTools.
nebtools = NEBTools(images)
nebfit = nebtools.get_fit()
x_pred = nebfit[0]
e_pred = nebfit[1]
x_fit = nebfit[2]
e_fit = nebfit[3]
e_barrier = np.max(e_pred)
print('Energy barrier:', e_barrier, 'eV')
u_pred = []
for i in images:
u_pred.append(i.info['uncertainty'])
fig, ax = plt.subplots(figsize=(8, 5))
prop_plots = dict(arrowstyle="<|-|>, head_width=0.5, head_length=1.",
connectionstyle="arc3",
color='teal',
ls='-',
lw=0.0)
ax.annotate("",
xy=(x_pred[np.argmax(e_pred)], np.max(e_pred)),
xycoords='data',
xytext=(x_pred[np.argmax(e_pred)], 0.0),
textcoords='data',
arrowprops=prop_plots)
prop_plots = dict(arrowstyle="|-|",
connectionstyle="arc3",
ls='-',
lw=2.,
color='teal')
ax.annotate("",
xy=(x_pred[np.argmax(e_pred)], np.max(e_pred)),
xycoords='data',
xytext=(x_pred[np.argmax(e_pred)], 0.0),
textcoords='data',
arrowprops=prop_plots)
ax.annotate(s=str(np.round(e_barrier, 3)) + ' eV',
xy=(x_pred[np.argmax(e_pred)], np.max(e_pred) / 1.65),
xycoords='data',
fontsize=15.0,
textcoords='data',
ha='right',
rotation=90,
color='teal')
ax.plot(x_fit, e_fit, color='black', linestyle='--', linewidth=1.5)
ax.errorbar(x_pred,
e_pred,
yerr=u_pred,
alpha=0.8,
markersize=0.0,
ecolor='midnightblue',
ls='',
elinewidth=3.0,
capsize=1.0)
ax.plot(x_pred,
e_pred,
color='firebrick',
alpha=0.7,
marker='o',
markersize=13.0,
markeredgecolor='black',
ls='')
ax.set_xlabel('Path distance ($\AA$)')
ax.set_ylabel('Energy (eV)')
plt.tight_layout(h_pad=1)
print('Saving pdf file with the NEB profile in file: ' + 'MLNEB.pdf')
plt.savefig('./' 'MLNEB.pdf', format='pdf')
plt.show()
if view_path is True:
print('Visualizing NEB images in ASE...')
view(images)
