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from ga_fcc_alloys_relax import relax
from ase.ga.convergence import GenerationRepetitionConvergence
from ase.ga.data import DataConnection
from ase.ga.element_crossovers import OnePointElementCrossover
from ase.ga.element_mutations import RandomElementMutation
from ase.ga.offspring_creator import OperationSelector
from ase.ga.population import Population
# Specify the number of generations this script will run
num_gens = 40
db = DataConnection('fcc_alloys.db')
ref_db = 'refs.db'
# Retrieve saved parameters
population_size = db.get_param('population_size')
metals = db.get_param('metals')
# Specify the procreation operators for the algorithm
# Try and play with the mutation operators that move to nearby
# places in the periodic table
oclist = (
[1, 1],
[RandomElementMutation(metals), OnePointElementCrossover(metals)],
)
operation_selector = OperationSelector(*oclist)
# Pass parameters to the population instance
pop = Population(data_connection=db, population_size=population_size)
# We form generations in this algorithm run and can therefore set
# a convergence criteria based on generations
cc = GenerationRepetitionConvergence(pop, 3)
# Relax the starting population
while db.get_number_of_unrelaxed_candidates() > 0:
a = db.get_an_unrelaxed_candidate()
relax(a, ref_db)
db.add_relaxed_step(a)
pop.update()
# Run the algorithm
for _ in range(num_gens):
if cc.converged():
print('converged')
break
for i in range(population_size):
a1, a2 = pop.get_two_candidates(with_history=False)
op = operation_selector.get_operator()
a3, desc = op.get_new_individual([a1, a2])
db.add_unrelaxed_candidate(a3, description=desc)
relax(a3, ref_db)
db.add_relaxed_step(a3)
pop.update()
# Print the current population to monitor the evolution
print(['-'.join(p.get_chemical_symbols()) for p in pop.pop])
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