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import time
from random import random
from ase.ga.cutandsplicepairing import CutAndSplicePairing
from ase.ga.data import DataConnection
from ase.ga.offspring_creator import OperationSelector
from ase.ga.parallellocalrun import ParallelLocalRun
from ase.ga.population import Population
from ase.ga.standard_comparators import InteratomicDistanceComparator
from ase.ga.standardmutations import (
MirrorMutation,
PermutationMutation,
RattleMutation,
)
from ase.ga.utilities import closest_distances_generator, get_all_atom_types
from ase.io import write
population_size = 20
mutation_probability = 0.3
n_to_test = 100
# Initialize the different components of the GA
da = DataConnection('gadb.db')
tmp_folder = 'tmp_folder/'
# An extra object is needed to handle the parallel execution
parallel_local_run = ParallelLocalRun(
data_connection=da, tmp_folder=tmp_folder, n_simul=4, calc_script='calc.py'
)
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()
parallel_local_run.relax(a)
# Wait until the starting population is relaxed
while parallel_local_run.get_number_of_jobs_running() > 0:
time.sleep(5.0)
# create the population
population = Population(
data_connection=da, population_size=population_size, comparator=comp
)
# test n_to_test new candidates
for i in range(n_to_test):
print(f'Now starting configuration number {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 random() < 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
parallel_local_run.relax(a3)
population.update()
# Wait until the last candidates are relaxed
while parallel_local_run.get_number_of_jobs_running() > 0:
time.sleep(5.0)
write('all_candidates.traj', da.get_all_relaxed_candidates())
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