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
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.))
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.))
