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 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()
# 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")
def neural_neb_ase(reactantxyzfile,
productxyzfile,
nff_dir,
rxn_name,
steps=500,
n_images=24,
fmax=0.004,
isclimb=False):
#reactant and products as ase Atoms
initial = AtomsBatch(xyz_to_ase_atoms(reactantxyzfile),
cutoff=5.5,
nbr_torch=True,
directed=True)
final = AtomsBatch(xyz_to_ase_atoms(productxyzfile),
cutoff=5.5,
nbr_torch=True,
directed=True)
# Make a band consisting of n_images:
images = [initial]
images += [initial.copy() for i in range(n_images)]
images += [final]
neb = SingleCalculatorNEB(images, k=0.02, climb=isclimb)
neb.method = 'improvedtangent'
# Interpolate linearly the potisions of the n_images:
neb.interpolate()
neb.idpp_interpolate(optimizer=BFGS, steps=steps)
images = read('idpp.traj@-{}:'.format(str(n_images + 2)))
# # Set calculators:
nff_ase = NeuralFF.from_file(nff_dir, device='cuda:0')
neb.set_calculators(nff_ase)
# # Optimize:
optimizer = BFGS(neb, trajectory='{}/{}.traj'.format(nff_dir, rxn_name))
optimizer.run(fmax=fmax, steps=steps)
# Read NEB images from File
images = read('{}/{}.traj@-{}:'.format(nff_dir, rxn_name,
str(n_images + 2)))
return images
dyn.run(fmax=0.05)
#view(initial)
# create final
final = initial.copy()
final.set_calculator(EMT())
final.set_constraint(constraint)
final[-2].position = final[-1].position
final[-1].x = d
final[-1].y = d / sqrt(3)
dyn = Optimizer(final)
dyn.run(fmax=0.1)
#view(final)
# create 2 intermediate step neb
neb = SingleCalculatorNEB([initial, final])
neb.refine(2)
neb.set_calculators(EMT())
assert neb.n() == 4
dyn = Optimizer(neb, maxstep=0.04, trajectory='mep_2coarse.traj')
dyn.run(fmax=0.1)
#dyn.run(fmax=39.1)
# read from the trajectory
neb = SingleCalculatorNEB('mep_2coarse.traj@-4:')
# refine in the important region
neb.refine(2, 1, 3)
neb.set_calculators(EMT())
dyn = Optimizer(neb, maxstep=0.04, trajectory='mep_2fine.traj')
def set_calculators(all=False):
c = GPAW(h=.3,
convergence={
'eigenstates': 0.1,
'energy': 0.1,
'density': 0.01
},
txt=txt)
# c = EMT()
n = len(images)
if not all:
n -= 2
neb.set_calculators([c] * n)
images = [mol]
for i in range(4):
images.append(images[0].copy())
images[-1].positions[2, 1] = 2 - images[0].positions[2, 1]
neb = SingleCalculatorNEB(images)
neb.interpolate()
for image in images:
print(image[2].position)
set_calculators(True)
dyn = FIRE(neb, trajectory='mep.traj')
dyn.insert_observer(set_calculators)
print(dyn.run(fmax=8.))
dyn.run(fmax=0.05)
#view(initial)
# create final
final = initial.copy()
final.set_calculator(EMT())
final.set_constraint(constraint)
final[-2].position = final[-1].position
final[-1].x = d
final[-1].y = d / sqrt(3)
dyn = Optimizer(final)
dyn.run(fmax=0.1)
#view(final)
# create 2 intermediate step neb
neb = SingleCalculatorNEB([initial, final])
neb.refine(2)
neb.set_calculators(EMT())
assert neb.n() == 4
dyn = Optimizer(neb, maxstep=0.04, trajectory='mep_2coarse.traj')
dyn.run(fmax=0.1)
#dyn.run(fmax=39.1)
# read from the trajectory
neb = SingleCalculatorNEB('mep_2coarse.traj@-4:')
# refine in the important region
neb.refine(2, 1, 3)
neb.set_calculators(EMT())
dyn = Optimizer(neb, maxstep=0.04, trajectory='mep_2fine.traj')
mol = Cluster([Atom('H'),
Atom('H',[1,0,0]),
Atom('H',[.5,.5,.5])],
cell = [2,2,2],
pbc=True)
def set_calculators(all=False):
c=GPAW(h=.3, convergence={'eigenstates':0.1,
'energy' : 0.1,
'density' : 0.01}, txt=txt)
# c = EMT()
n = len(images)
if not all:
n -= 2
neb.set_calculators([c] * n)
images = [mol]
for i in range(4):
images.append(images[0].copy())
images[-1].positions[2, 1] = 2 - images[0].positions[2, 1]
neb = SingleCalculatorNEB(images)
neb.interpolate()
for image in images:
print(image[2].position)
set_calculators(True)
dyn = FIRE(neb, trajectory='mep.traj')
dyn.insert_observer(set_calculators)
print(dyn.run(fmax=8.))
dyn.run(fmax=0.05)
# view(initial)
print('Relax final image')
final = initial.copy()
final.set_calculator(EMT())
final.set_constraint(constraint)
final[-2].position = final[-1].position
final[-1].x = d
final[-1].y = d / sqrt(3)
dyn = Optimizer(final)
dyn.run(fmax=0.1)
# view(final)
print('Create neb with 2 intermediate steps')
neb = SingleCalculatorNEB([initial, final])
neb.refine(2)
assert neb.n() == 4
print('Optimize neb using a single calculator')
neb.set_calculators(EMT())
# print('0001', id(neb.images[0]), id(neb.images[0].get_calculator().atoms))
dyn = Optimizer(neb, maxstep=0.04, trajectory='mep_2coarse.traj')
dyn.run(fmax=0.1)
# dyn.run(fmax=39.1)
print('Optimize neb using a many calculators')
neb = SingleCalculatorNEB([initial, final])
neb.refine(2)
neb.set_calculators([EMT() for i in range(neb.n())])
dyn = Optimizer(neb, maxstep=0.04, trajectory='mep_2coarse.traj')
def test_COCu111_2():
Optimizer = BFGS
# Distance between Cu atoms on a (111) surface:
a = 3.6
d = a / sqrt(2)
fcc111 = Atoms(symbols='Cu',
cell=[(d, 0, 0), (d / 2, d * sqrt(3) / 2, 0),
(d / 2, d * sqrt(3) / 6, -a / sqrt(3))],
pbc=True)
initial = fcc111 * (2, 2, 4)
initial.set_cell([2 * d, d * sqrt(3), 1])
initial.set_pbc((1, 1, 0))
initial.calc = EMT()
Z = initial.get_positions()[:, 2]
indices = [i for i, z in enumerate(Z) if z < Z.mean()]
constraint = FixAtoms(indices=indices)
initial.set_constraint(constraint)
print('Relax initial image')
dyn = Optimizer(initial)
dyn.run(fmax=0.05)
Z = initial.get_positions()[:, 2]
print(Z[0] - Z[1])
print(Z[1] - Z[2])
print(Z[2] - Z[3])
b = 1.2
h = 1.5
initial += Atom('C', (d / 2, -b / 2, h))
initial += Atom('O', (d / 2, +b / 2, h))
dyn = Optimizer(initial)
dyn.run(fmax=0.05)
# view(initial)
print('Relax final image')
final = initial.copy()
final.calc = EMT()
final.set_constraint(constraint)
final[-2].position = final[-1].position
final[-1].x = d
final[-1].y = d / sqrt(3)
dyn = Optimizer(final)
dyn.run(fmax=0.1)
# view(final)
print('Create neb with 2 intermediate steps')
neb = SingleCalculatorNEB([initial, final])
neb.refine(2)
assert neb.n() == 4
print('Optimize neb using a single calculator')
neb.set_calculators(EMT())
# print('0001', id(neb.images[0]), id(neb.images[0].calc.atoms))
dyn = Optimizer(neb, maxstep=0.04, trajectory='mep_2coarse.traj')
dyn.run(fmax=0.1)
# dyn.run(fmax=39.1)
print('Optimize neb using a many calculators')
neb = SingleCalculatorNEB([initial, final])
neb.refine(2)
neb.set_calculators([EMT() for i in range(neb.n())])
dyn = Optimizer(neb, maxstep=0.04, trajectory='mep_2coarse.traj')
dyn.run(fmax=0.1)
# dyn.run(fmax=39.1)
# read from the trajectory
neb = SingleCalculatorNEB('mep_2coarse.traj', index='-4:')
# refine in the important region
neb.refine(2, 1, 3)
neb.set_calculators(EMT())
print('Optimize refined neb using a single calculator')
dyn = Optimizer(neb, maxstep=0.04, trajectory='mep_2fine.traj')
dyn.run(fmax=0.1)
assert len(neb.images) == 8
