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)
# create the population
population = Population(data_connection=da,
population_size=population_size,
use_tags=True)
softmut = SoftMutation(blmin,
bounds=[2., 5.],
used_modes_file=None,
use_tags=True)
rotmut = RotationalMutation(blmin, fraction=0.3, min_angle=0.5 * np.pi)
rattlemut = RattleMutation(blmin,
n_top,
rattle_prop=0.3,
rattle_strength=0.5,
use_tags=True,
test_dist_to_slab=False)
rattlerotmut = RattleRotationalMutation(rattlemut, rotmut)
permut = PermutationMutation(n_top,
probability=0.33,
test_dist_to_slab=False,
use_tags=True,
blmin=blmin)
combmut = CombinationMutation(rattlemut, rotmut, verbose=True)
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)
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():
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]})
sg = StartGenerator(blocks, blmin, volume, cellbounds=cellbounds,
splits=splits)
# 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
pairing = CutAndSplicePairing(blmin, p1=1., p2=0., minfrac=0.15,
cellbounds=cellbounds, use_tags=True)
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,
use_tags=True)
softmut = SoftMutation(blmin, bounds=[2., 5.], used_modes_file=None,
use_tags=True)
rotmut = RotationalMutation(blmin, fraction=0.3, min_angle=0.5 * np.pi)
rattlemut = RattleMutation(blmin, n_top, rattle_prop=0.3, rattle_strength=0.5,
use_tags=True, test_dist_to_slab=False)
rattlerotmut = RattleRotationalMutation(rattlemut, rotmut)
permut = PermutationMutation(n_top, probability=0.33, test_dist_to_slab=False,
use_tags=True, blmin=blmin)
combmut = CombinationMutation(rattlemut, rotmut, verbose=True)
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_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)
assert len(slab) == len(slab2)
assert np.all(slab.get_positions() == slab2.get_positions())
dp = np.sum((top2.get_positions() - top1.get_positions())**2, axis=1)**0.5
# check that all displacements are smaller than the rattle strength we
# cannot check if 40 % of the structures have been rattled since it is
# probabilistic and because the probability will be lower if two atoms
# get too close
for p in dp:
assert p < 0.8 * 3**0.5
# now we check the permutation mutation
mmut = PermutationMutation(n_top, probability=0.5)
c3, desc = mmut.get_new_individual([c1])
assert np.all(c1.numbers == c3.numbers)
top1 = c1[-n_top:]
top2 = c3[-n_top:]
slab2 = c3[0:(len(c1) - n_top)]
assert len(slab) == len(slab2)
assert np.all(slab.get_positions() == slab2.get_positions())
dp = np.sum((top2.get_positions() - top1.get_positions())**2, axis=1)**0.5
# verify that two positions have been changed
assert len(dp[dp > 0]) == 2
def test_mutations(seed):
from ase.ga.startgenerator import StartGenerator
from ase.ga.utilities import closest_distances_generator
from ase.ga.standardmutations import RattleMutation, PermutationMutation
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]
n_top = len(atom_numbers)
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
# first verify that the rattle mutation works
rmut = RattleMutation(blmin, n_top, rattle_strength=0.8, rattle_prop=0.4,
rng=rng)
c2, desc = rmut.get_new_individual([c1])
assert np.all(c1.numbers == c2.numbers)
top1 = c1[-n_top:]
top2 = c2[-n_top:]
slab2 = c2[0:(len(c1) - n_top)]
assert len(slab) == len(slab2)
assert np.all(slab.get_positions() == slab2.get_positions())
dp = np.sum((top2.get_positions() - top1.get_positions())**2, axis=1)**0.5
# check that all displacements are smaller than the rattle strength we
# cannot check if 40 % of the structures have been rattled since it is
# probabilistic and because the probability will be lower if two atoms
# get too close
for p in dp:
assert p < 0.8 * 3**0.5
# now we check the permutation mutation
mmut = PermutationMutation(n_top, probability=0.5, rng=rng)
c3, desc = mmut.get_new_individual([c1])
assert np.all(c1.numbers == c3.numbers)
top1 = c1[-n_top:]
top2 = c3[-n_top:]
slab2 = c3[0:(len(c1) - n_top)]
assert len(slab) == len(slab2)
assert np.all(slab.get_positions() == slab2.get_positions())
dp = np.sum((top2.get_positions() - top1.get_positions())**2, axis=1)**0.5
# verify that two positions have been changed
assert len(dp[dp > 0]) == 2
