def run_ga(n_to_test):
"""
This method specifies how to run the GA once the
initial random structures have been stored in godb.db.
"""
# Various initializations:
population_size = 10 # maximal size of the population
da = DataConnection('godb.db')
atom_numbers_to_optimize = da.get_atom_numbers_to_optimize() # = [14] * 7
n_to_optimize = len(atom_numbers_to_optimize) # = 7
# This defines how close the Si atoms are allowed to get
# in candidate structures generated by the genetic operators:
blmin = closest_distances_generator(atom_numbers_to_optimize,
ratio_of_covalent_radii=0.4)
# This is our OFPComparator instance which will be
# used to judge whether or not two structures are identical:
comparator = OFPComparator(n_top=None, dE=1.0, cos_dist_max=1e-3,
rcut=10., binwidth=0.05, pbc=[False]*3,
sigma=0.1, nsigma=4, recalculate=False)
# Defining a typical combination of genetic operators:
pairing = CutAndSplicePairing(da.get_slab(), n_to_optimize, blmin)
rattlemut = RattleMutation(blmin, n_to_optimize, rattle_prop=0.8,
rattle_strength=1.5)
operators = OperationSelector([2., 1.], [pairing, rattlemut])
# Relax the randomly generated initial candidates:
while da.get_number_of_unrelaxed_candidates() > 0:
a = da.get_an_unrelaxed_candidate()
a = relax_one(a)
da.add_relaxed_step(a)
# Create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comparator,
logfile='log.txt')
current_pop = population.get_current_population()
# Test n_to_test new candidates
for step in range(n_to_test):
print('Starting configuration number %d' % step, flush=True)
a3 = None
while a3 is None:
a1, a2 = population.get_two_candidates()
a3, description = operators.get_new_individual([a1, a2])
da.add_unrelaxed_candidate(a3, description=description)
a3 = relax_one(a3)
da.add_relaxed_step(a3)
population.update()
best = population.get_current_population()[0]
print('Highest raw score at this point: %.3f' % get_raw_score(best))
print('GA finished after step %d' % step)
write('all_candidates.traj', da.get_all_relaxed_candidates())
write('current_population.traj', population.get_current_population())
def test_chain_operators(seed):
from ase.ga.startgenerator import StartGenerator
from ase.ga.cutandsplicepairing import CutAndSplicePairing
from ase.ga.standardmutations import StrainMutation
from ase.ga.utilities import (closest_distances_generator, atoms_too_close,
CellBounds)
import numpy as np
from ase import Atoms
# set up the random number generator
rng = np.random.RandomState(seed)
slab = Atoms('', cell=(0, 16, 16), pbc=[True, False, False])
blocks = ['C'] * 8
n_top = 8
use_tags = False
num_vcv = 1
box_volume = 8. * n_top
blmin = closest_distances_generator(atom_numbers=[6],
ratio_of_covalent_radii=0.6)
cellbounds = CellBounds(
bounds={
'phi': [0.1 * 180., 0.9 * 180.],
'chi': [0.1 * 180., 0.9 * 180.],
'psi': [0.1 * 180., 0.9 * 180.],
'a': [1, 6]
})
box_to_place_in = [[None, 6., 6.], [None, [0., 4., 0.], [0., 0., 4.]]]
sg = StartGenerator(slab,
blocks,
blmin,
box_volume=box_volume,
splits=None,
box_to_place_in=box_to_place_in,
number_of_variable_cell_vectors=num_vcv,
cellbounds=cellbounds,
test_too_far=True,
test_dist_to_slab=False,
rng=rng)
parents = []
for i in range(2):
a = None
while a is None:
a = sg.get_new_candidate()
a.info['confid'] = i
parents.append(a)
assert len(a) == n_top
assert len(np.unique(a.get_tags())) == 8
assert np.allclose(a.get_pbc(), slab.get_pbc())
p = a.get_positions()
assert np.min(p[:, 1:]) > 6.
assert np.max(p[:, 1:]) < 6. + 4.
assert not atoms_too_close(a, blmin, use_tags=use_tags)
c = a.get_cell()
assert np.allclose(c[1:], slab.get_cell()[1:])
assert cellbounds.is_within_bounds(c)
v = a.get_volume() * (4. / 16.)**2
assert abs(v - box_volume) < 1e-5
# Test cut-and-splice pairing and strain mutation
pairing = CutAndSplicePairing(slab,
n_top,
blmin,
number_of_variable_cell_vectors=num_vcv,
p1=1.,
p2=0.,
minfrac=0.15,
cellbounds=cellbounds,
use_tags=use_tags,
rng=rng)
strainmut = StrainMutation(blmin,
cellbounds=cellbounds,
number_of_variable_cell_vectors=num_vcv,
use_tags=use_tags,
rng=rng)
strainmut.update_scaling_volume(parents)
for operator in [pairing, strainmut]:
child = None
while child is None:
child, desc = operator.get_new_individual(parents)
assert not atoms_too_close(child, blmin, use_tags=use_tags)
cell = child.get_cell()
assert cellbounds.is_within_bounds(cell)
assert np.allclose(cell[1:], slab.get_cell()[1:])
# Initialize the different components of the GA
da = DataConnection('gadb.db')
atom_numbers_to_optimize = da.get_atom_numbers_to_optimize()
n_to_optimize = len(atom_numbers_to_optimize)
slab = da.get_slab()
all_atom_types = get_all_atom_types(slab, atom_numbers_to_optimize)
blmin = closest_distances_generator(all_atom_types,
ratio_of_covalent_radii=0.7)
comp = InteratomicDistanceComparator(n_top=n_to_optimize,
pair_cor_cum_diff=0.015,
pair_cor_max=0.7,
dE=0.02,
mic=False)
pairing = CutAndSplicePairing(slab, n_to_optimize, blmin)
mutations = OperationSelector([1., 1., 1.], [
MirrorMutation(blmin, n_to_optimize),
RattleMutation(blmin, n_to_optimize),
PermutationMutation(n_to_optimize)
])
# Relax all unrelaxed structures (e.g. the starting population)
while da.get_number_of_unrelaxed_candidates() > 0:
a = da.get_an_unrelaxed_candidate()
a.set_calculator(EMT())
print('Relaxing starting candidate {0}'.format(a.info['confid']))
dyn = BFGS(a, trajectory=None, logfile=None)
dyn.run(fmax=0.05, steps=100)
a.info['key_value_pairs']['raw_score'] = -a.get_potential_energy()
da.add_relaxed_step(a)
# Change the following three parameters to suit your needs
population_size = 5
mutation_probability = 0.3
n_to_test = 5
# Initialize the different components of the GA
da = DataConnection("gadb.db")
atom_numbers_to_optimize = da.get_atom_numbers_to_optimize()
n_to_optimize = len(atom_numbers_to_optimize)
slab = da.get_slab()
all_atom_types = get_all_atom_types(slab, atom_numbers_to_optimize)
blmin = closest_distances_generator(all_atom_types, ratio_of_covalent_radii=0.7)
comp = InteratomicDistanceComparator(n_top=n_to_optimize, pair_cor_cum_diff=0.015, pair_cor_max=0.7, dE=0.02, mic=False)
pairing = CutAndSplicePairing(slab, n_to_optimize, blmin)
mutations = OperationSelector(
[1.0, 1.0, 1.0],
[MirrorMutation(blmin, n_to_optimize), RattleMutation(blmin, n_to_optimize), PermutationMutation(n_to_optimize)],
)
# Relax all unrelaxed structures (e.g. the starting population)
while da.get_number_of_unrelaxed_candidates() > 0:
a = da.get_an_unrelaxed_candidate()
a.set_calculator(EMT())
print("Relaxing starting candidate {0}".format(a.info["confid"]))
dyn = BFGS(a, trajectory=None, logfile=None)
dyn.run(fmax=0.05, steps=100)
a.set_raw_score(-a.get_potential_energy())
da.add_relaxed_step(a)
da = DataConnection('gadb.db')
# Various items needed for initializing the genetic operators
slab = da.get_slab()
atom_numbers_to_optimize = da.get_atom_numbers_to_optimize()
n_top = len(atom_numbers_to_optimize)
blmin = closest_distances_generator(atom_numbers_to_optimize, 1.0)
cellbounds = CellBounds(bounds={'phi': [30, 150], 'chi': [30, 150],
'psi': [30, 150]})
# Note the "use_tags" keyword argument being used
# to signal that we want to preserve molecular identity
# via the tags
pairing = CutAndSplicePairing(slab, n_top, blmin, p1=1., p2=0.,
minfrac=0.15, cellbounds=cellbounds,
number_of_variable_cell_vectors=3,
use_tags=True)
rattlemut = RattleMutation(blmin, n_top, rattle_prop=0.3, rattle_strength=0.5,
use_tags=True)
strainmut = StrainMutation(blmin, stddev=0.7, cellbounds=cellbounds,
use_tags=True)
rotmut = RotationalMutation(blmin, fraction=0.3, min_angle=0.5 * np.pi)
rattlerotmut = RattleRotationalMutation(rattlemut, rotmut)
blmin_soft = closest_distances_generator(atom_numbers_to_optimize, 0.8)
softmut = SoftMutation(blmin_soft, bounds=[2., 5.], use_tags=True)
def test_film_operators(seed):
from ase.ga.startgenerator import StartGenerator
from ase.ga.cutandsplicepairing import CutAndSplicePairing
from ase.ga.standardmutations import StrainMutation
from ase.ga.utilities import (closest_distances_generator, atoms_too_close,
CellBounds)
import numpy as np
from ase import Atoms
from ase.build import molecule
# set up the random number generator
rng = np.random.RandomState(seed)
slab = Atoms('', cell=(0, 0, 15), pbc=[True, True, False])
cation, anion = 'Mg', molecule('OH')
d_oh = anion.get_distance(0, 1)
blocks = [(cation, 4), (anion, 8)]
n_top = 4 + 8 * len(anion)
use_tags = True
num_vcv = 2
box_volume = 8. * n_top
blmin = closest_distances_generator(atom_numbers=[1, 8, 12],
ratio_of_covalent_radii=0.6)
cellbounds = CellBounds(
bounds={
'phi': [0.1 * 180., 0.9 * 180.],
'chi': [0.1 * 180., 0.9 * 180.],
'psi': [0.1 * 180., 0.9 * 180.],
'a': [2, 8],
'b': [2, 8]
})
box_to_place_in = [[None, None, 3.], [None, None, [0., 0., 5.]]]
sg = StartGenerator(slab,
blocks,
blmin,
box_volume=box_volume,
splits={(2, 1): 1},
box_to_place_in=box_to_place_in,
number_of_variable_cell_vectors=num_vcv,
cellbounds=cellbounds,
test_too_far=True,
test_dist_to_slab=False,
rng=rng)
parents = []
for i in range(2):
a = None
while a is None:
a = sg.get_new_candidate()
a.info['confid'] = i
parents.append(a)
assert len(a) == n_top
assert len(np.unique(a.get_tags())) == 4 + 8
assert np.allclose(a.get_pbc(), slab.get_pbc())
p = a.get_positions()
assert np.min(p[:, 2]) > 3. - 0.5 * d_oh
assert np.max(p[:, 2]) < 3. + 5. + 0.5 * d_oh
assert not atoms_too_close(a, blmin, use_tags=use_tags)
c = a.get_cell()
assert np.allclose(c[2], slab.get_cell()[2])
assert cellbounds.is_within_bounds(c)
v = a.get_volume() * 5. / 15.
assert abs(v - box_volume) < 1e-5
# Test cut-and-splice pairing and strain mutation
pairing = CutAndSplicePairing(slab,
n_top,
blmin,
number_of_variable_cell_vectors=num_vcv,
p1=1.,
p2=0.,
minfrac=0.15,
cellbounds=cellbounds,
use_tags=use_tags,
rng=rng)
strainmut = StrainMutation(blmin,
cellbounds=cellbounds,
number_of_variable_cell_vectors=num_vcv,
use_tags=use_tags,
rng=rng)
strainmut.update_scaling_volume(parents)
for operator in [pairing, strainmut]:
child = None
while child is None:
child, desc = operator.get_new_individual(parents)
assert not atoms_too_close(child, blmin, use_tags=use_tags)
cell = child.get_cell()
assert cellbounds.is_within_bounds(cell)
assert np.allclose(cell[2], slab.get_cell()[2])
def test_cutandsplicepairing(seed):
from ase.ga.startgenerator import StartGenerator
from ase.ga.utilities import closest_distances_generator, atoms_too_close
from ase.ga.cutandsplicepairing import CutAndSplicePairing
import numpy as np
from ase.build import fcc111
from ase.constraints import FixAtoms
# set up the random number generator
rng = np.random.RandomState(seed)
# first create two random starting candidates
slab = fcc111('Au', size=(4, 4, 2), vacuum=10.0, orthogonal=True)
slab.set_constraint(FixAtoms(mask=slab.positions[:, 2] <= 10.))
pos = slab.get_positions()
cell = slab.get_cell()
p0 = np.array([0., 0., max(pos[:, 2]) + 2.])
v1 = cell[0, :] * 0.8
v2 = cell[1, :] * 0.8
v3 = cell[2, :]
v3[2] = 3.
blmin = closest_distances_generator(atom_numbers=[47, 79],
ratio_of_covalent_radii=0.7)
atom_numbers = 2 * [47] + 2 * [79]
sg = StartGenerator(slab=slab,
blocks=atom_numbers,
blmin=blmin,
box_to_place_in=[p0, [v1, v2, v3]],
rng=rng)
c1 = sg.get_new_candidate()
c1.info['confid'] = 1
c2 = sg.get_new_candidate()
c2.info['confid'] = 2
n_top = len(atom_numbers)
pairing = CutAndSplicePairing(slab, n_top, blmin, rng=rng)
c3, desc = pairing.get_new_individual([c1, c2])
# verify that the stoichiometry is preserved
assert np.all(c3.numbers == c1.numbers)
top1 = c1[-n_top:]
top2 = c2[-n_top:]
top3 = c3[-n_top:]
# verify that the positions in the new candidate come from c1 or c2
n1 = -1 * np.ones((n_top, ))
n2 = -1 * np.ones((n_top, ))
for i in range(n_top):
for j in range(n_top):
if np.allclose(top1.positions[j, :], top3.positions[i, :], 1e-12):
n1[i] = j
break
elif np.allclose(top2.positions[j, :], top3.positions[i, :],
1e-12):
n2[i] = j
break
assert (n1[i] > -1 and n2[i] == -1) or (n1[i] == -1 and n2[i] > -1)
# verify that c3 includes atoms from both c1 and c2
assert len(n1[n1 > -1]) > 0 and len(n2[n2 > -1]) > 0
# verify no atoms too close
assert not atoms_too_close(top3, blmin)
cd = closest_distances_generator(atom_numbers=[47, 79], ratio_of_covalent_radii=0.7)
atom_numbers = 2 * [47] + 2 * [79]
sg = StartGenerator(
slab=slab, atom_numbers=atom_numbers, closest_allowed_distances=cd, box_to_place_in=[p0, [v1, v2, v3]]
)
c1 = sg.get_new_candidate()
c1.info["confid"] = 1
c2 = sg.get_new_candidate()
c2.info["confid"] = 2
n_top = len(atom_numbers)
pairing = CutAndSplicePairing(slab, n_top, cd)
c3, desc = pairing.get_new_individual([c1, c2])
# verify that the stoichiometry is preserved
assert np.all(c3.numbers == c1.numbers)
top1 = c1[-n_top:]
top2 = c2[-n_top:]
top3 = c3[-n_top:]
# verify that the positions in the new candidate come from c1 or c2
n1 = -1 * np.ones((n_top,))
n2 = -1 * np.ones((n_top,))
for i in range(n_top):
for j in range(n_top):
atom_numbers = 2 * [47] + 2 * [79]
sg = StartGenerator(slab=slab,
atom_numbers=atom_numbers,
closest_allowed_distances=cd,
box_to_place_in=[p0, [v1, v2, v3]])
c1 = sg.get_new_candidate()
c1.info['confid'] = 1
c2 = sg.get_new_candidate()
c2.info['confid'] = 2
n_top = len(atom_numbers)
pairing = CutAndSplicePairing(slab, n_top, cd)
c3, desc = pairing.get_new_individual([c1, c2])
# verify that the stoichiometry is preserved
assert np.all(c3.numbers == c1.numbers)
top1 = c1[-n_top:]
top2 = c2[-n_top:]
top3 = c3[-n_top:]
# verify that the positions in the new candidate come from c1 or c2
n1 = -1 * np.ones((n_top, ))
n2 = -1 * np.ones((n_top, ))
for i in range(n_top):
for j in range(n_top):
if np.all(top1.positions[j, :] == top3.positions[i, :]):
def run_ga(n_to_test, kptdensity=None):
''' This method specifies how to run the GA once the
initial random structures have been stored in godb.db.
'''
# Various initializations:
population_size = 10
da = DataConnection('godb.db')
atom_numbers_to_optimize = da.get_atom_numbers_to_optimize()
n_to_optimize = len(atom_numbers_to_optimize)
slab = da.get_slab()
all_atom_types = get_all_atom_types(slab, atom_numbers_to_optimize)
blmin = closest_distances_generator(all_atom_types,
ratio_of_covalent_radii=0.05)
# Defining the mix of genetic operators:
mutation_probability = 0.3333
pairing = CutAndSplicePairing(slab, n_to_optimize, blmin)
rattlemut = RattleMutation(blmin, n_to_optimize,
rattle_prop=0.8, rattle_strength=1.5)
mirrormut = MirrorMutation(blmin, n_to_optimize)
mutations = OperationSelector([1., 1.], [rattlemut, mirrormut])
if True:
# Recalculate raw scores of any relaxed candidates
# present in the godb.db database (only applies to
# iter007).
structures = da.get_all_relaxed_candidates()
for atoms in structures:
atoms = singlepoint(atoms)
da.c.delete([atoms.info['relax_id']])
if 'data' not in atoms.info:
atoms.info['data'] = {}
da.add_relaxed_step(atoms)
print('Finished recalculating raw scores')
# Relax the randomly generated initial candidates:
while da.get_number_of_unrelaxed_candidates() > 0:
a = da.get_an_unrelaxed_candidate()
a.wrap()
a = relax_one(a)
da.add_relaxed_step(a)
# Create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comparator,
logfile='log.txt')
current_pop = population.get_current_population()
# Test n_to_test new candidates
ga_raw_scores = []
step = 0
for step in range(n_to_test):
print('Starting configuration number %d' % step, flush=True)
clock = time()
a3 = None
r = random()
if r > mutation_probability:
while a3 is None:
a1, a2 = population.get_two_candidates()
a3, desc = pairing.get_new_individual([a1, a2])
else:
while a3 is None:
a1 = population.get_one_candidate()
a3, desc = mutations.get_new_individual([a1])
dt = time() - clock
op = 'pairing' if r > mutation_probability else 'mutating'
print('Time for %s candidate(s): %.3f' % (op, dt), flush=True)
a3.wrap()
da.add_unrelaxed_candidate(a3, description=desc)
a3 = relax_one(a3)
da.add_relaxed_step(a3)
# Various updates:
population.update()
current_pop = population.get_current_population()
write('current_population.traj', current_pop)
# Print out information for easy analysis/plotting afterwards:
if r > mutation_probability:
print('Step %d %s %.3f %.3f %.3f' % (step, desc,\
get_raw_score(a1), get_raw_score(a2), get_raw_score(a3)))
else:
print('Step %d %s %.3f %.3f' % (step, desc,\
get_raw_score(a1), get_raw_score(a3)))
print('Step %d highest raw score in pop: %.3f' % \
(step, get_raw_score(current_pop[0])))
ga_raw_scores.append(get_raw_score(a3))
print('Step %d highest raw score generated by GA: %.3f' % \
(step, max(ga_raw_scores)))
emin = population.pop[0].get_potential_energy()
print('GA finished after step %d' % step)
print('Lowest energy = %8.3f eV' % emin, flush=True)
write('all_candidates.traj', da.get_all_relaxed_candidates())
write('current_population.traj', population.get_current_population())
def test_basic_example_main_run(seed, testdir):
# set up the random number generator
rng = np.random.RandomState(seed)
# create the surface
slab = fcc111('Au', size=(4, 4, 1), vacuum=10.0, orthogonal=True)
slab.set_constraint(FixAtoms(mask=len(slab) * [True]))
# define the volume in which the adsorbed cluster is optimized
# the volume is defined by a corner position (p0)
# and three spanning vectors (v1, v2, v3)
pos = slab.get_positions()
cell = slab.get_cell()
p0 = np.array([0., 0., max(pos[:, 2]) + 2.])
v1 = cell[0, :] * 0.8
v2 = cell[1, :] * 0.8
v3 = cell[2, :]
v3[2] = 3.
# Define the composition of the atoms to optimize
atom_numbers = 2 * [47] + 2 * [79]
# define the closest distance two atoms of a given species can be to each other
unique_atom_types = get_all_atom_types(slab, atom_numbers)
blmin = closest_distances_generator(atom_numbers=unique_atom_types,
ratio_of_covalent_radii=0.7)
# create the starting population
sg = StartGenerator(slab=slab,
blocks=atom_numbers,
blmin=blmin,
box_to_place_in=[p0, [v1, v2, v3]],
rng=rng)
# generate the starting population
population_size = 5
starting_population = [sg.get_new_candidate() for i in range(population_size)]
# from ase.visualize import view # uncomment these lines
# view(starting_population) # to see the starting population
# create the database to store information in
d = PrepareDB(db_file_name=db_file,
simulation_cell=slab,
stoichiometry=atom_numbers)
for a in starting_population:
d.add_unrelaxed_candidate(a)
# XXXXXXXXXX This should be the beginning of a new test,
# but we are using some resources from the precious part.
# Maybe refactor those things as (module-level?) fixtures.
# Change the following three parameters to suit your needs
population_size = 5
mutation_probability = 0.3
n_to_test = 5
# Initialize the different components of the GA
da = DataConnection('gadb.db')
atom_numbers_to_optimize = da.get_atom_numbers_to_optimize()
n_to_optimize = len(atom_numbers_to_optimize)
slab = da.get_slab()
all_atom_types = get_all_atom_types(slab, atom_numbers_to_optimize)
blmin = closest_distances_generator(all_atom_types,
ratio_of_covalent_radii=0.7)
comp = InteratomicDistanceComparator(n_top=n_to_optimize,
pair_cor_cum_diff=0.015,
pair_cor_max=0.7,
dE=0.02,
mic=False)
pairing = CutAndSplicePairing(slab, n_to_optimize, blmin, rng=rng)
mutations = OperationSelector([1., 1., 1.],
[MirrorMutation(blmin, n_to_optimize, rng=rng),
RattleMutation(blmin, n_to_optimize, rng=rng),
PermutationMutation(n_to_optimize, rng=rng)],
rng=rng)
# Relax all unrelaxed structures (e.g. the starting population)
while da.get_number_of_unrelaxed_candidates() > 0:
a = da.get_an_unrelaxed_candidate()
a.calc = EMT()
print('Relaxing starting candidate {0}'.format(a.info['confid']))
dyn = BFGS(a, trajectory=None, logfile=None)
dyn.run(fmax=0.05, steps=100)
set_raw_score(a, -a.get_potential_energy())
da.add_relaxed_step(a)
# create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comp,
rng=rng)
# test n_to_test new candidates
for i in range(n_to_test):
print('Now starting configuration number {0}'.format(i))
a1, a2 = population.get_two_candidates()
a3, desc = pairing.get_new_individual([a1, a2])
if a3 is None:
continue
da.add_unrelaxed_candidate(a3, description=desc)
# Check if we want to do a mutation
if rng.rand() < mutation_probability:
a3_mut, desc = mutations.get_new_individual([a3])
if a3_mut is not None:
da.add_unrelaxed_step(a3_mut, desc)
a3 = a3_mut
# Relax the new candidate
a3.calc = EMT()
dyn = BFGS(a3, trajectory=None, logfile=None)
dyn.run(fmax=0.05, steps=100)
set_raw_score(a3, -a3.get_potential_energy())
da.add_relaxed_step(a3)
population.update()
write('all_candidates.traj', da.get_all_relaxed_candidates())
def test_bulk_operators(seed):
# set up the random number generator
rng = np.random.RandomState(seed)
h2 = Atoms('H2', positions=[[0, 0, 0], [0, 0, 0.75]])
blocks = [('H', 4), ('H2O', 3), (h2, 2)] # the building blocks
volume = 40. * sum([x[1] for x in blocks]) # cell volume in angstrom^3
splits = {(2,): 1, (1,): 1} # cell splitting scheme
stoichiometry = []
for block, count in blocks:
if type(block) == str:
stoichiometry += list(Atoms(block).numbers) * count
else:
stoichiometry += list(block.numbers) * count
atom_numbers = list(set(stoichiometry))
blmin = closest_distances_generator(atom_numbers=atom_numbers,
ratio_of_covalent_radii=1.3)
cellbounds = CellBounds(bounds={'phi': [30, 150], 'chi': [30, 150],
'psi': [30, 150], 'a': [3, 50],
'b': [3, 50], 'c': [3, 50]})
slab = Atoms('', pbc=True)
sg = StartGenerator(slab, blocks, blmin, box_volume=volume,
number_of_variable_cell_vectors=3,
cellbounds=cellbounds, splits=splits, rng=rng)
# Generate 2 candidates
a1 = sg.get_new_candidate()
a1.info['confid'] = 1
a2 = sg.get_new_candidate()
a2.info['confid'] = 2
# Define and test genetic operators
n_top = len(a1)
pairing = CutAndSplicePairing(slab, n_top, blmin, p1=1., p2=0., minfrac=0.15,
number_of_variable_cell_vectors=3,
cellbounds=cellbounds, use_tags=True, rng=rng)
a3, desc = pairing.get_new_individual([a1, a2])
cell = a3.get_cell()
assert cellbounds.is_within_bounds(cell)
assert not atoms_too_close(a3, blmin, use_tags=True)
n_top = len(a1)
strainmut = StrainMutation(blmin, stddev=0.7, cellbounds=cellbounds,
number_of_variable_cell_vectors=3,
use_tags=True, rng=rng)
softmut = SoftMutation(blmin, bounds=[2., 5.], used_modes_file=None,
use_tags=True) # no rng
rotmut = RotationalMutation(blmin, fraction=0.3, min_angle=0.5 * np.pi,
rng=rng)
rattlemut = RattleMutation(blmin, n_top, rattle_prop=0.3, rattle_strength=0.5,
use_tags=True, test_dist_to_slab=False, rng=rng)
rattlerotmut = RattleRotationalMutation(rattlemut, rotmut) # no rng
permut = PermutationMutation(n_top, probability=0.33, test_dist_to_slab=False,
use_tags=True, blmin=blmin, rng=rng)
combmut = CombinationMutation(rattlemut, rotmut, verbose=True) # no rng
mutations = [strainmut, softmut, rotmut,
rattlemut, rattlerotmut, permut, combmut]
for i, mut in enumerate(mutations):
a = [a1, a2][i % 2]
a3 = None
while a3 is None:
a3, desc = mut.get_new_individual([a])
cell = a3.get_cell()
assert cellbounds.is_within_bounds(cell)
assert np.all(a3.numbers == a.numbers)
assert not atoms_too_close(a3, blmin, use_tags=True)
modes_file = 'modes.txt'
softmut_with = SoftMutation(blmin, bounds=[2., 5.], use_tags=True,
used_modes_file=modes_file) # no rng
no_muts = 3
for _ in range(no_muts):
softmut_with.get_new_individual([a1])
softmut_with.read_used_modes(modes_file)
assert len(list(softmut_with.used_modes.values())[0]) == no_muts
os.remove(modes_file)
comparator = OFPComparator(recalculate=True)
gold = bulk('Au') * (2, 2, 2)
assert comparator.looks_like(gold, gold)
# This move should not exceed the default threshold
gc = gold.copy()
gc[0].x += .1
assert comparator.looks_like(gold, gc)
# An additional step will exceed the threshold
gc[0].x += .2
assert not comparator.looks_like(gold, gc)
