def finalize(atoms, energy=None, forces=None, stress=None):
# Finalizes the atoms by attaching a SinglePointCalculator
# and setting the raw score as the negative of the total energy
atoms.wrap()
calc = SinglePointCalculator(atoms,
energy=energy,
forces=forces,
stress=stress)
atoms.calc = calc
raw_score = -atoms.get_potential_energy()
set_raw_score(atoms, raw_score)
def test_standardcomparator():
from ase.ga.standard_comparators import (InteratomicDistanceComparator,
EnergyComparator,
RawScoreComparator,
SequentialComparator)
from ase import Atoms
from ase.calculators.singlepoint import SinglePointCalculator
from ase.ga import set_raw_score
a1 = Atoms('AgAgAg', positions=[[0, 0, 0], [1.5, 0, 0], [1.5, 1.5, 0]])
a2 = Atoms('AgAgAg', positions=[[0, 0, 0], [1.4, 0, 0], [1.5, 1.5, 0]])
e1 = 1.0
e2 = 0.8
a1.set_calculator(SinglePointCalculator(a1, energy=e1))
a2.set_calculator(SinglePointCalculator(a2, energy=e2))
comp1 = InteratomicDistanceComparator(n_top=3,
pair_cor_cum_diff=0.03,
pair_cor_max=0.7,
dE=0.3)
assert comp1.looks_like(a1, a2)
comp2 = InteratomicDistanceComparator(n_top=3,
pair_cor_cum_diff=0.03,
pair_cor_max=0.7,
dE=0.15)
assert not comp2.looks_like(a1, a2)
comp3 = InteratomicDistanceComparator(n_top=3,
pair_cor_cum_diff=0.02,
pair_cor_max=0.7,
dE=0.3)
assert not comp3.looks_like(a1, a2)
hard_E_comp = EnergyComparator(dE=1.0)
assert hard_E_comp.looks_like(a1, a2)
soft_E_comp = EnergyComparator(dE=.01)
assert not soft_E_comp.looks_like(a1, a2)
set_raw_score(a1, .1)
set_raw_score(a2, .27)
rs_comp = RawScoreComparator(0.15)
assert not rs_comp.looks_like(a1, a2)
comp1 = SequentialComparator([hard_E_comp, rs_comp], [0, 0])
assert not comp1.looks_like(a1, a2)
comp2 = SequentialComparator([hard_E_comp, rs_comp], [0, 1])
assert comp2.looks_like(a1, a2)
def finalize(atoms, energy=None, forces=None, stress=None):
""" Saves attributes by attaching a SinglePointCalculator
and sets the default raw score (equal to the negative
of the potential energy).
"""
atoms.wrap()
calc = SinglePointCalculator(atoms,
energy=energy,
forces=forces,
stress=stress)
atoms.set_calculator(calc)
raw_score = -atoms.get_potential_energy()
set_raw_score(atoms, raw_score)
def test_add_candidates():
import pytest
from ase.build import fcc111
from ase.ga.data import PrepareDB
from ase.ga.data import DataConnection
from ase.ga.offspring_creator import OffspringCreator
from ase.ga import set_raw_score
import os
db_file = 'gadb.db'
if os.path.isfile(db_file):
os.remove(db_file)
db = PrepareDB(db_file)
slab1 = fcc111('Ag', size=(2, 2, 2))
db.add_unrelaxed_candidate(slab1)
slab2 = fcc111('Cu', size=(2, 2, 2))
set_raw_score(slab2, 4)
db.add_relaxed_candidate(slab2)
assert slab2.info['confid'] == 3
db = DataConnection(db_file)
assert db.get_number_of_unrelaxed_candidates() == 1
slab3 = db.get_an_unrelaxed_candidate()
old_confid = slab3.info['confid']
slab3[0].symbol = 'Au'
db.add_unrelaxed_candidate(slab3, 'mutated: Parent {0}'.format(old_confid))
new_confid = slab3.info['confid']
# confid should update when using add_unrelaxed_candidate
assert old_confid != new_confid
slab3[1].symbol = 'Au'
db.add_unrelaxed_step(slab3, 'mutated: Parent {0}'.format(new_confid))
# confid should not change when using add_unrelaxed_step
assert slab3.info['confid'] == new_confid
with pytest.raises(AssertionError):
db.add_relaxed_step(slab3)
set_raw_score(slab3, 3)
db.add_relaxed_step(slab3)
slab4 = OffspringCreator.initialize_individual(slab1,
fcc111('Au', size=(2, 2, 2)))
set_raw_score(slab4, 67)
db.add_relaxed_candidate(slab4)
assert slab4.info['confid'] == 7
more_slabs = []
for m in ['Ni', 'Pd', 'Pt']:
slab = fcc111(m, size=(2, 2, 2))
slab = OffspringCreator.initialize_individual(slab1, slab)
set_raw_score(slab, sum(slab.get_masses()))
more_slabs.append(slab)
db.add_more_relaxed_candidates(more_slabs)
assert more_slabs[1].info['confid'] == 9
os.remove(db_file)
# Pass parameters to the population instance
# A variable_function is required to divide candidates into groups here we use
# the chemical composition
pop = RankFitnessPopulation(data_connection=db,
population_size=pop_size,
variable_function=get_comp)
# Evaluate the starting population
# The only requirement of the evaluation is to set the raw_score
# Negative mixing energy means more stable than the pure slabs
# The optimization always progress towards larger raw score,
# so we take the negative mixing energy as the raw score
print('Evaluating initial candidates')
while db.get_number_of_unrelaxed_candidates() > 0:
a = db.get_an_unrelaxed_candidate()
set_raw_score(a, -get_mixing_energy(a))
db.add_relaxed_step(a)
pop.update()
# Below is the iterative part of the algorithm
gen_num = db.get_generation_number()
for i in range(num_gens):
print('Creating and evaluating generation {0}'.format(gen_num + i))
new_generation = []
for _ in range(pop_size):
# Select parents for a new candidate
parents = pop.get_two_candidates()
# Select an operator and use it
op = operation_selector.get_operator()
offspring, desc = op.get_new_individual(parents)
syms = a2.get_chemical_symbols()
assert 'Ba' in syms
assert len(set(syms)) == 3
op = MoveUpMutation(cations, 1, 1.)
a3, desc = op.get_new_individual([a2])
syms = a3.get_chemical_symbols()
assert 'Ba' not in syms
assert len(set(syms)) == 2
cations = ['Co', 'Ni', 'Cu']
a1 = Atoms('NiNiBrBr')
a1.info['confid'] = 1
op = MoveRightMutation(cations, 1, 1.)
a2, desc = op.get_new_individual([a1])
a2.info['confid'] = 2
syms = a2.get_chemical_symbols()
assert len(set(syms)) == 2
assert len([i for i in syms if i == 'Cu']) == 2
op = MoveLeftMutation(cations, 2, .5)
a3, desc = op.get_new_individual([a2])
syms = a3.get_chemical_symbols()
from ase.ga import set_raw_score, get_raw_score
assert len(set(syms)) == 3
set_raw_score(a3, 5.0)
assert get_raw_score(a3) == 5.0
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)
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)
# 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)
# and now for the actual test dc = DataConnection(db_file) slab_get = dc.get_slab() an_get = dc.get_atom_numbers_to_optimize() assert dc.get_number_of_unrelaxed_candidates() == 20 a1 = dc.get_an_unrelaxed_candidate() dc.mark_as_queued(a1) assert dc.get_number_of_unrelaxed_candidates() == 19 assert len(dc.get_all_candidates_in_queue()) == 1 set_raw_score(a1, 0.0) dc.add_relaxed_step(a1) assert dc.get_number_of_unrelaxed_candidates() == 19 assert len(dc.get_all_candidates_in_queue()) == 0 assert len(dc.get_all_relaxed_candidates()) == 1 a2 = dc.get_an_unrelaxed_candidate() dc.mark_as_queued(a2) confid = a2.info['confid'] assert dc.get_all_candidates_in_queue()[0] == confid dc.remove_from_queue(confid) assert len(dc.get_all_candidates_in_queue()) == 0
dE=0.15)
assert not comp2.looks_like(a1, a2)
comp3 = InteratomicDistanceComparator(n_top=3,
pair_cor_cum_diff=0.02,
pair_cor_max=0.7,
dE=0.3)
assert not comp3.looks_like(a1, a2)
hard_E_comp = EnergyComparator(dE=1.0)
assert hard_E_comp.looks_like(a1, a2)
soft_E_comp = EnergyComparator(dE=.01)
assert not soft_E_comp.looks_like(a1, a2)
set_raw_score(a1, .1)
set_raw_score(a2, .27)
rs_comp = RawScoreComparator(0.15)
assert not rs_comp.looks_like(a1, a2)
comp1 = SequentialComparator([hard_E_comp, rs_comp], [0, 0])
assert not comp1.looks_like(a1, a2)
comp2 = SequentialComparator([hard_E_comp, rs_comp], [0, 1])
assert comp2.looks_like(a1, a2)
syms = a2.get_chemical_symbols()
assert 'Ba' in syms
assert len(set(syms)) == 3
op = MoveUpMutation(cations, 1, 1.)
a3, desc = op.get_new_individual([a2])
syms = a3.get_chemical_symbols()
assert 'Ba' not in syms
assert len(set(syms)) == 2
cations = ['Co', 'Ni', 'Cu']
a1 = Atoms('NiNiBrBr')
a1.info['confid'] = 1
op = MoveRightMutation(cations, 1, 1.)
a2, desc = op.get_new_individual([a1])
a2.info['confid'] = 2
syms = a2.get_chemical_symbols()
assert len(set(syms)) == 2
assert len([i for i in syms if i == 'Cu']) == 2
op = MoveLeftMutation(cations, 2, .5)
a3, desc = op.get_new_individual([a2])
syms = a3.get_chemical_symbols()
from ase.ga import set_raw_score, get_raw_score
assert len(set(syms)) == 3
set_raw_score(a3, 5.0)
assert get_raw_score(a3) == 5.0
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)
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)
# 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)
from ase.ga.offspring_creator import OffspringCreator
from ase.ga import set_raw_score
import os
db_file = 'gadb.db'
if os.path.isfile(db_file):
os.remove(db_file)
db = PrepareDB(db_file)
slab1 = fcc111('Ag', size=(2, 2, 2))
db.add_unrelaxed_candidate(slab1)
slab2 = fcc111('Cu', size=(2, 2, 2))
set_raw_score(slab2, 4)
db.add_relaxed_candidate(slab2)
assert slab2.info['confid'] == 3
db = DataConnection(db_file)
assert db.get_number_of_unrelaxed_candidates() == 1
slab3 = db.get_an_unrelaxed_candidate()
old_confid = slab3.info['confid']
slab3[0].symbol = 'Au'
db.add_unrelaxed_candidate(slab3, 'mutated: Parent {0}'.format(old_confid))
new_confid = slab3.info['confid']
# confid should update when using add_unrelaxed_candidate
assert old_confid != new_confid
slab3[1].symbol = 'Au'
db.add_unrelaxed_step(slab3, 'mutated: Parent {0}'.format(new_confid))
def test_element_operators(seed):
import numpy as np
from ase import Atoms
from ase.ga.element_crossovers import OnePointElementCrossover
# set up the random number generator
rng = np.random.RandomState(seed)
a1 = Atoms('SrSrSrBaClClClClBrBrBrBr')
a1.info['confid'] = 1
a2 = Atoms('CaCaMgBaFFFFFFFF')
a2.info['confid'] = 2
cations = ['Sr', 'Ba', 'Ca', 'Mg']
anions = ['Cl', 'F', 'Br']
op = OnePointElementCrossover([cations, anions], [3, 2], [.25, .5],
rng=rng)
a3, desc = op.get_new_individual([a1, a2])
syms = a3.get_chemical_symbols()
assert len(set([i for i in syms if i in cations])) < 4
assert len(set([i for i in syms if i in anions])) < 3
from ase.ga.element_mutations import RandomElementMutation
op = RandomElementMutation([cations, anions], [3, 2], [.25, .5], rng=rng)
a4, desc = op.get_new_individual([a1])
syms = a4.get_chemical_symbols()
assert len(set([i for i in syms if i in cations])) < 4
assert len(set([i for i in syms if i in anions])) < 3
op = RandomElementMutation(anions, 2, .5, rng=rng)
a4, desc = op.get_new_individual([a2])
syms = a4.get_chemical_symbols()
assert len(set([i for i in syms if i in anions])) == 2
from ase.ga.element_mutations import MoveDownMutation
from ase.ga.element_mutations import MoveUpMutation
from ase.ga.element_mutations import MoveRightMutation
from ase.ga.element_mutations import MoveLeftMutation
a1 = Atoms('SrSrClClClCl')
a1.info['confid'] = 1
op = MoveDownMutation(cations, 2, .5, rng=rng)
a2, desc = op.get_new_individual([a1])
a2.info['confid'] = 2
syms = a2.get_chemical_symbols()
assert 'Ba' in syms
assert len(set(syms)) == 3
op = MoveUpMutation(cations, 1, 1., rng=rng)
a3, desc = op.get_new_individual([a2])
syms = a3.get_chemical_symbols()
assert 'Ba' not in syms
assert len(set(syms)) == 2
cations = ['Co', 'Ni', 'Cu']
a1 = Atoms('NiNiBrBr')
a1.info['confid'] = 1
op = MoveRightMutation(cations, 1, 1., rng=rng)
a2, desc = op.get_new_individual([a1])
a2.info['confid'] = 2
syms = a2.get_chemical_symbols()
assert len(set(syms)) == 2
assert len([i for i in syms if i == 'Cu']) == 2
op = MoveLeftMutation(cations, 2, .5, rng=rng)
a3, desc = op.get_new_individual([a2])
syms = a3.get_chemical_symbols()
from ase.ga import set_raw_score, get_raw_score
assert len(set(syms)) == 3
set_raw_score(a3, 5.0)
assert get_raw_score(a3) == 5.0
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())
for m in metals:
slab = fcc111(m,
size=(2, 4, 3),
a=lattice_constants[m],
vacuum=5,
orthogonal=True)
slab.set_calculator(EMT())
# We save the reference energy as E_A / N
e = slab.get_potential_energy()
e_per_atom = e / len(slab)
refs[m] = e_per_atom
print('{0} = {1:.3f} eV/atom'.format(m, e_per_atom))
# The mixing energy for the pure slab is 0 by definition
set_raw_score(slab, 0.0)
pure_slabs.append(slab)
# The population size should be at least the number of different compositions
pop_size = 2 * len(slab)
# We prepare the db and write a few constants that we are going to use later
db = PrepareDB('hull.db',
population_size=pop_size,
reference_energies=refs,
metals=metals,
lattice_constants=lattice_constants)
# We add the pure slabs to the database as relaxed because we have already
# set the raw_score
for slab in pure_slabs:
def test_database_logic(seed, testdir):
from ase.ga.data import PrepareDB
from ase.ga.data import DataConnection
from ase.ga.startgenerator import StartGenerator
from ase.ga.utilities import closest_distances_generator
from ase.ga import set_raw_score
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)
slab = fcc111('Au', size=(4, 4, 2), vacuum=10.0, orthogonal=True)
slab.set_constraint(FixAtoms(mask=slab.positions[:, 2] <= 10.))
# 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 closest distance between two atoms of a given species
blmin = closest_distances_generator(atom_numbers=[47, 79],
ratio_of_covalent_radii=0.7)
# Define the composition of the atoms to optimize
atom_numbers = 2 * [47] + 2 * [79]
# 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
starting_population = [sg.get_new_candidate() for i in range(20)]
d = PrepareDB(db_file_name=db_file,
simulation_cell=slab,
stoichiometry=atom_numbers)
for a in starting_population:
d.add_unrelaxed_candidate(a)
# and now for the actual test
dc = DataConnection(db_file)
dc.get_slab()
dc.get_atom_numbers_to_optimize()
assert dc.get_number_of_unrelaxed_candidates() == 20
a1 = dc.get_an_unrelaxed_candidate()
dc.mark_as_queued(a1)
assert dc.get_number_of_unrelaxed_candidates() == 19
assert len(dc.get_all_candidates_in_queue()) == 1
set_raw_score(a1, 0.0)
dc.add_relaxed_step(a1)
assert dc.get_number_of_unrelaxed_candidates() == 19
assert len(dc.get_all_candidates_in_queue()) == 0
assert len(dc.get_all_relaxed_candidates()) == 1
a2 = dc.get_an_unrelaxed_candidate()
dc.mark_as_queued(a2)
confid = a2.info['confid']
assert dc.get_all_candidates_in_queue()[0] == confid
dc.remove_from_queue(confid)
assert len(dc.get_all_candidates_in_queue()) == 0
comp2 = InteratomicDistanceComparator(n_top=3,
pair_cor_cum_diff=0.03,
pair_cor_max=0.7,
dE=0.15)
assert not comp2.looks_like(a1, a2)
comp3 = InteratomicDistanceComparator(n_top=3,
pair_cor_cum_diff=0.02,
pair_cor_max=0.7,
dE=0.3)
assert not comp3.looks_like(a1, a2)
hard_E_comp = EnergyComparator(dE=1.0)
assert hard_E_comp.looks_like(a1, a2)
soft_E_comp = EnergyComparator(dE=.01)
assert not soft_E_comp.looks_like(a1, a2)
set_raw_score(a1, .1)
set_raw_score(a2, .27)
rs_comp = RawScoreComparator(0.15)
assert not rs_comp.looks_like(a1, a2)
comp1 = SequentialComparator([hard_E_comp, rs_comp], [0, 0])
assert not comp1.looks_like(a1, a2)
comp2 = SequentialComparator([hard_E_comp, rs_comp], [0, 1])
assert comp2.looks_like(a1, a2)
