def test_neb_methods(method, optimizer, precon, optmethod, ref_vacancy,
setup_images):
# unpack the reference result
Ef_ref, dE_ref, saddle_ref = ref_vacancy
# now relax the MEP for comparison
images, _, _ = setup_images
fmax_history = []
def save_fmax_history(mep):
fmax_history.append(mep.get_residual())
k = 0.1
if precon == 'Exp':
k = 0.01
mep = NEB(images, k=k, method=method, precon=precon)
if optmethod is not None:
opt = optimizer(mep, method=optmethod)
else:
opt = optimizer(mep)
opt.attach(save_fmax_history, 1, mep)
opt.run(fmax=1e-2)
nebtools = NEBTools(images)
Ef, dE = nebtools.get_barrier(fit=False)
print(f'{method},{optimizer.__name__},{precon} '
f'=> Ef = {Ef:.3f}, dE = {dE:.3f}')
forcefit = fit_images(images)
output_dir = os.path.dirname(__file__)
with open(
f'{output_dir}/MEP_{method}_{optimizer.__name__}_{optmethod}'
f'_{precon}.json', 'w') as f:
json.dump(
{
'fmax_history': fmax_history,
'method': method,
'optmethod': optmethod,
'precon': precon,
'optimizer': optimizer.__name__,
'path': forcefit.path,
'energies': forcefit.energies.tolist(),
'fit_path': forcefit.fit_path.tolist(),
'fit_energies': forcefit.fit_energies.tolist(),
'lines': np.array(forcefit.lines).tolist(),
'Ef': Ef,
'dE': dE
}, f)
centre = 2 # we have 5 images total, so central image has index 2
vdiff, _ = find_mic(images[centre].positions - saddle_ref.positions,
images[centre].cell)
print(f'Ef error {Ef - Ef_ref} dE error {dE - dE_ref} '
f'position error at saddle {abs(vdiff).max()}')
assert abs(Ef - Ef_ref) < 1e-2
assert abs(dE - dE_ref) < 1e-2
assert abs(vdiff).max() < 1e-2
def run(self, calc, filename):
"""
Runs NEB calculations.
Parameters
----------
calc: object. Calculator to be used to run method.
filename: str. Label to save generated trajectory files."""
initial = self.starting_images[0].copy()
final = self.starting_images[-1].copy()
if self.ml2relax:
# Relax initial and final images
ml_initial = initial
ml_initial.set_calculator(calc)
ml_final = final
ml_final.set_calculator(calc)
print("BUILDING INITIAL")
qn = BFGS(ml_initial,
trajectory="initial.traj",
logfile="initial_relax_log.txt")
qn.run(fmax=0.01, steps=100)
print("BUILDING FINAL")
qn = BFGS(ml_final,
trajectory="final.traj",
logfile="final_relax_log.txt")
qn.run(fmax=0.01, steps=100)
initial = ml_initial.copy()
final = ml_final.copy()
initial.set_calculator(calc)
final.set_calculator(calc)
images = [initial]
for i in range(self.intermediate_samples):
image = initial.copy()
image.set_calculator(calc)
images.append(image)
images.append(final)
print("NEB BEING BUILT")
neb = SingleCalculatorNEB(images)
neb.interpolate()
print("NEB BEING OPTIMISED")
opti = BFGS(neb,
trajectory=filename + ".traj",
logfile="al_neb_log.txt")
opti.run(fmax=0.01, steps=100)
print("NEB DONE")
"""
The following code is used to visualise the NEB at every iteration
"""
built_neb = NEBTools(images)
barrier, dE = built_neb.get_barrier()
# max_force = built_neb.get_fmax()
# fig = built_neb.plot_band()
plt.show()
def prepare_graph(transition_states, lmdf, trandir, log='@-7:'):
Ed = []
for ts in transition_states:
outputs = read(trandir + ts + log)
for output in outputs:
add_tip4p_const(output)
nebtools = NEBTools(outputs)
Ef, dE = nebtools.get_barrier()
Ed.append([ts[:-5], Ef, Ed])
Ed = pd.DataFrame(Ed, columns=['images', 'Ed', 'deltaE'])
mins = Ed['images'].apply(lambda x: x.split("_"))
min1 = []
min2 = []
revers = []
for line in mins:
min1.append(line[0])
min2.append(line[1])
revers.append(line[1] + "_" + line[0])
db = pd.DataFrame(columns=["m1", "m2", "Ed", "images"])
db['m1'] = min1
db['m2'] = min2
db['Ed'] = Ed['Ed']
db['images'] = Ed['images']
db = db.merge(lmdf[['m2', 'E2']], how='left', on='m2')
db = db.merge(lmdf[['m1', 'E1']], how='left', on='m1')
db['ET'] = db['Ed'] + db['E1']
db2 = pd.DataFrame(columns=["m1", "m2", "Ed", "images", "E2", "E1", "ET"])
db2['m1'] = db["m2"]
db2['m2'] = db["m1"]
db2['ET'] = db['ET']
db2['Ed'] = db['Ed']
db2['E1'] = db["E2"]
db2['E2'] = db["E1"]
db2['images'] = revers
new = pd.concat([db, db2])
new = new[new["ET"] <= 0]
new = new[new["E1"] <= new["ET"]]
new = new[new["E2"] <= new["ET"]]
dbnp = new.values
g = nx.Graph()
g.add_nodes_from(new['m1'])
g.add_nodes_from(new['m2'])
for line in dbnp:
line[3] = line[0][4:] + '_' + line[1][4:]
if line[2] != np.nan:
g.add_edge(line[0], line[1], ts=[line[0], line[1], line[6]])
return g, new
def _ref_vacancy_global(_setup_images_global):
# use distance from moving atom to one of its neighbours as reaction coord
# relax intermediate image to the saddle point using a bondlength constraint
images, i1, i2 = _setup_images_global
initial, saddle, final = (images[0].copy(), images[2].copy(),
images[4].copy())
initial.calc = calc()
saddle.calc = calc()
final.calc = calc()
saddle.set_constraint(FixBondLength(i1, i2))
opt = ODE12r(saddle)
opt.run(fmax=1e-2)
nebtools = NEBTools([initial, saddle, final])
Ef_ref, dE_ref = nebtools.get_barrier(fit=False)
print('REF:', Ef_ref, dE_ref)
return Ef_ref, dE_ref, saddle
def test_autoneb(asap3):
EMT = asap3.EMT
fmax = 0.02
# Pt atom adsorbed in a hollow site:
slab = fcc211('Pt', size=(3, 2, 2), vacuum=4.0)
add_adsorbate(slab, 'Pt', 0.5, (-0.1, 2.7))
# Fix second and third layers:
slab.set_constraint(FixAtoms(range(6, 12)))
# Use EMT potential:
slab.calc = EMT()
# Initial state:
qn = QuasiNewton(slab, trajectory='neb000.traj')
qn.run(fmax=fmax)
# Final state:
slab[-1].x += slab.get_cell()[0, 0]
slab[-1].y += 2.8
qn = QuasiNewton(slab, trajectory='neb001.traj')
qn.run(fmax=fmax)
# Stops PermissionError on Win32 for access to
# the traj file that remains open.
del qn
def attach_calculators(images):
for i in range(len(images)):
images[i].calc = EMT()
autoneb = AutoNEB(attach_calculators,
prefix='neb',
optimizer='BFGS',
n_simul=3,
n_max=7,
fmax=fmax,
k=0.5,
parallel=False,
maxsteps=[50, 1000])
autoneb.run()
nebtools = NEBTools(autoneb.all_images)
assert abs(nebtools.get_barrier()[0] - 0.937) < 1e-3
def test_ethene_rotation(tmpdir):
tmpdir.chdir()
# Optimise molecule
initial = molecule('C2H6')
smart_cell(initial, vac=4.0, h=0.01)
initial.set_calculator(iEspresso(pw=300, dw=4000, kpts='gamma'))
qn = QuasiNewton(initial, 'initial.traj')
qn.run(fmax=0.01)
# Create final state
final = initial.copy()
final.positions[2:5] = initial.positions[[3, 4, 2]]
final.set_calculator(iEspresso(pw=300, dw=4000, kpts='gamma'))
final.get_potential_energy()
# Generate blank images
images = [initial]
nimage = 7
for i in range(nimage):
image = initial.copy()
image.set_calculator(iEspresso(pw=300, dw=4000, kpts='gamma'))
images.append(image)
images.append(final)
# Run IDPP interpolation
neb = NEBEspresso(images)
neb.interpolate('idpp')
# Run NEB calculation
qn = QuasiNewton(neb, logfile='ethane_linear.log', trajectory='neb.traj')
qn.run(fmax=0.05)
nt = NEBTools(neb.images)
print('fmax: ', nt.get_fmax())
print('Ef, dE: ', nt.get_barrier())
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
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)
# 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
neb.idpp_interpolate(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)
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):
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
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')
# 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
neb.idpp_interpolate(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)
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
print(" -> set calculator")
for image in images:
image.set_calculator(
PySCF_simple(atoms=image, method='MP2', basis='6-31g*'))
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)
qn.run(fmax=0.05)
# Final state:
slab[-1].x += slab.get_cell()[0, 0]
slab[-1].y += 2.8
qn = QuasiNewton(slab, trajectory='neb001.traj')
qn.run(fmax=0.05)
# Stops PermissionError on Win32 for access to
# the traj file that remains open.
del qn
def attach_calculators(images):
for i in range(len(images)):
images[i].set_calculator(EMT())
autoneb = AutoNEB(attach_calculators,
prefix='neb',
n_simul=3,
n_max=7,
fmax=0.05,
k=0.5,
parallel=False,
maxsteps=[50, 1000])
autoneb.run()
nebtools = NEBTools(autoneb.all_images)
assert abs(nebtools.get_barrier()[0] - 0.938) < 1e-3
# Generate blank images.
images = [initial]
for i in range(9):
images.append(initial.copy())
for image in images:
image.calc = EMT()
images.append(final)
neb = NEB(images, climb=True)
neb.interpolate(method='idpp') #idpp插值，设置初猜
# set calculator
for atoms in images:
atoms.calc = EMT()
atoms.get_potential_energy()
# Optimize:
py_fname = os.path.splitext(sys.argv[0])[0]
traj_fname = "{}.traj".format(py_fname)
log_fname = "{}.log".format(py_fname)
optimizer = FIRE(neb, trajectory=traj_fname, logfile=log_fname)
optimizer.run(fmax=0.04)
neb_result = list(iread(traj_fname))
for i in neb_result:
#print(i.get_potential_energy())
pass
neb_result = NEBTools(neb_result)
neb_result.plot_bands(True, True, label=py_fname)
print(neb_result.get_barrier(), neb_result.get_fmax())
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
# Final state:
slab[-1].x += slab.get_cell()[0, 0]
slab[-1].y += 2.8
qn = QuasiNewton(slab, trajectory='neb001.traj')
qn.run(fmax=0.05)
# Stops PermissionError on Win32 for access to
# the traj file that remains open.
del qn
def attach_calculators(images):
for i in range(len(images)):
images[i].set_calculator(EMT())
autoneb = AutoNEB(attach_calculators,
prefix='neb',
optimizer='BFGS',
n_simul=3,
n_max=7,
fmax=0.05,
k=0.5,
parallel=False,
maxsteps=[50, 1000])
autoneb.run()
nebtools = NEBTools(autoneb.all_images)
assert abs(nebtools.get_barrier()[0] - 0.938) < 1e-3
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()
