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
# 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')
dyn.run(fmax=0.1)
assert len(neb.images) == 8
# 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')
dyn.run(fmax=0.1)
assert len(neb.images) == 8
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')
dyn.run(fmax=0.1)
# dyn.run(fmax=39.1)
# read from the trajectory
neb = SingleCalculatorNEB('mep_2coarse.traj@-4:')
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')
dyn.run(fmax=0.1)
# dyn.run(fmax=39.1)
# read from the trajectory
neb = SingleCalculatorNEB('mep_2coarse.traj@-4:')
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
