import numpy as np
from ase.ga.population import RankFitnessPopulation
from ase.ga.data import DataConnection
from ase.ga.offspring_creator import OperationSelector
from ase.ga.slab_operators import (CutSpliceSlabCrossover,
RandomSlabPermutation,
RandomCompositionMutation)
from ase.ga import set_raw_score
from ase.calculators.emt import EMT
# Connect to the database containing all candidates
db = DataConnection('hull.db')
# Retrieve saved parameters
pop_size = db.get_param('population_size')
refs = db.get_param('reference_energies')
metals = db.get_param('metals')
lattice_constants = db.get_param('lattice_constants')
def get_mixing_energy(atoms):
# Set the correct cell size from the lattice constant
new_a = get_avg_lattice_constant(atoms.get_chemical_symbols())
# Use the orthogonal fcc cell to find the current lattice constant
current_a = atoms.cell[0][0] / np.sqrt(2)
atoms.set_cell(atoms.cell * new_a / current_a, scale_atoms=True)
# Calculate the energy
atoms.set_calculator(EMT())
e = atoms.get_potential_energy()
from ase.ga.element_mutations import RandomElementMutation
from ase.ga.element_crossovers import OnePointElementCrossover
from ase.ga.offspring_creator import OperationSelector
from ase.ga.population import Population
from ase.ga.convergence import GenerationRepetitionConvergence
from ga_fcc_alloys_relax import relax
# 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
from ase.ga.element_mutations import RandomElementMutation
from ase.ga.element_crossovers import OnePointElementCrossover
from ase.ga.offspring_creator import OperationSelector
from ase.ga.population import Population
from ase.ga.convergence import GenerationRepetitionConvergence
from ga_fcc_alloys_relax import relax
# 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
