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import numpy as np
from ga_molecular_crystal_relax import relax
from ase.ga import get_raw_score
from ase.ga.cutandsplicepairing import CutAndSplicePairing
from ase.ga.data import DataConnection
from ase.ga.offspring_creator import OperationSelector
from ase.ga.ofp_comparator import OFPComparator
from ase.ga.population import Population
from ase.ga.soft_mutation import SoftMutation
from ase.ga.standardmutations import (
RattleMutation,
RattleRotationalMutation,
RotationalMutation,
StrainMutation,
)
from ase.ga.utilities import CellBounds, closest_distances_generator
from ase.io import write
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=1.0,
p2=0.0,
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=[2.0, 5.0], use_tags=True)
operators = OperationSelector(
[5, 1, 1, 1, 1, 1],
[pairing, rattlemut, strainmut, rotmut, rattlerotmut, softmut],
)
# Relaxing the initial candidates
while da.get_number_of_unrelaxed_candidates() > 0:
a = da.get_an_unrelaxed_candidate()
relax(a)
da.add_relaxed_step(a)
# The structure comparator for the population
comp = OFPComparator(
n_top=n_top,
dE=1.0,
cos_dist_max=5e-3,
rcut=10.0,
binwidth=0.05,
pbc=[True, True, True],
sigma=0.05,
nsigma=4,
recalculate=False,
)
# The population
population = Population(
data_connection=da, population_size=10, comparator=comp, logfile='log.txt'
)
current_pop = population.get_current_population()
strainmut.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4)
pairing.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4)
# Test a few new candidates
n_to_test = 10
for step in range(n_to_test):
print(f'Now starting configuration number {step}', flush=True)
# Generate a new candidate
a3 = None
while a3 is None:
a1, a2 = population.get_two_candidates()
a3, desc = operators.get_new_individual([a1, a2])
# Relax it and add to database
da.add_unrelaxed_candidate(a3, description=desc)
relax(a3)
da.add_relaxed_step(a3)
# Update the population
population.update()
current_pop = population.get_current_population()
write('current_population.traj', current_pop)
# Update the strain mutation and pairing operators
if step % 10 == 0:
strainmut.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4)
pairing.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4)
# Print out information for easier follow-up/analysis/plotting:
print(
'Step %d %s %.3f %.3f %.3f'
% (step, desc, get_raw_score(a1), get_raw_score(a2), get_raw_score(a3))
)
print(
'Step %d highest raw score in pop: %.3f'
% (step, get_raw_score(current_pop[0]))
)
print('GA finished after step %d' % step)
hiscore = get_raw_score(current_pop[0])
print('Highest raw score = %8.4f eV' % hiscore)
write('all_candidates.traj', da.get_all_relaxed_candidates())
write('current_population.traj', current_pop)
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