Optimization with a Genetic Algorithm¶
A genetic algorithm (GA) has been implemented for global structure
optimization within ase. The optimizer consists of its own module
ase.ga which includes all classes needed for the optimizer.
The method was first described in the supplemental material of
L. B. Vilhelmsen and B. HammerPhysical Review Letters, Vol. 108 (Mar 2012), 126101
and a full account of the method is given in
L. B. Vilhelmsen and B. HammerJournal of Chemical Physics, Vol 141, 044711 (2014)
Any questions about how to use the GA can be asked at the mailing list.
A Brief Overview of the Implementation¶
The GA relies on the ase.db module for tracking which structures have been found. Before the GA optimization starts the user therefore needs to prepare this database and appropriate folders. This is done through an initialization script as the one described in the next section. In this initialization the starting population is generated and added to the database.
After initialization the main script is run. This script defines objects responsible for the different parts of the GA and then creates and locally relaxes new candidates. It is up to the user to define when the main script should terminate. An example of a main script is given in the next section. Notice that because of the persistent data storage the main script can be executed multiple times to generate new candidates.
The GA implementation generally follows a responsibility driven approach. This means that each part of the GA is isolated into individual classes making it possible to put together an optimizer satisfying the needs of a specific optimization problem.
This tutorial will use the following parts of the GA:
A population responsible for proposing new candidates to pair together.
A paring operator which combines two candidates.
A set of mutations.
A comparator which determines if two structures are different.
A starting population generator.
Each of the above components are described in the supplemental material of the first reference given above and will not be discussed here. The example will instead focus on the technical aspect of executing the GA.
A Basic Example¶
The user needs to specify the following three properties about the structure that needs to be optimized.
A list of atomic numbers for the structure to be optimized
A super cell in which to do the optimization. If the structure to optimize resides on a surface or in a support this supercell contains the atoms which should not be considered explicitly by the GA.
A box defining the volume of the super cell in which to randomly distribute the starting population.
As an example we will find the structure of a Ag2Au2 cluster on a Au(111) surface using the EMT optimizer.
The script doing all the initialisations should be run in the folder in which the GA optimisation is to take place. The script looks as follows:
from ase.ga.data import PrepareDB
from ase.ga.startgenerator import StartGenerator
from ase.ga.utilities import closest_distances_generator
from ase.ga.utilities import get_all_atom_types
from ase.constraints import FixAtoms
import numpy as np
from ase.build import fcc111
db_file = 'gadb.db'
# 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, atom_numbers, blmin,
box_to_place_in=[p0, [v1, v2, v3]])
# generate the starting population
population_size = 20
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)
Having initialized the GA optimization we now need to actually run the GA. The main script running the GA consists of first an initialization part, and then a loop proposing new structures and locally optimizing them. The main script can look as follows:
from random import random
from ase.io import write
from ase.optimize import BFGS
from ase.calculators.emt import EMT
from ase.ga.data import DataConnection
from ase.ga.population import Population
from ase.ga.standard_comparators import InteratomicDistanceComparator
from ase.ga.cutandsplicepairing import CutAndSplicePairing
from ase.ga.utilities import closest_distances_generator
from ase.ga.utilities import get_all_atom_types
from ase.ga.offspring_creator import OperationSelector
from ase.ga.standardmutations import MirrorMutation
from ase.ga.standardmutations import RattleMutation
from ase.ga.standardmutations import PermutationMutation
# Change the following three parameters to suit your needs
population_size = 20
mutation_probability = 0.3
n_to_test = 20
# 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)
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.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)
a.info['key_value_pairs']['raw_score'] = -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)
# 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
a3.calc = EMT()
dyn = BFGS(a3, trajectory=None, logfile=None)
dyn.run(fmax=0.05, steps=100)
a3.info['key_value_pairs']['raw_score'] = -a3.get_potential_energy()
da.add_relaxed_step(a3)
population.update()
write('all_candidates.traj', da.get_all_relaxed_candidates())
The above script proposes and locally relaxes 20 new candidates. To
speed up the execution of this sample the local relaxations are
limited to 100 steps. This restriction should not be set in a real
application. Note it is important to set the raw_score, as
it is what is being optimized (maximized). It is really an input in the
atoms.info['key_value_pairs'] dictionary.
The GA progress can be monitored by running the tool
ase/ga/tools/get_all_candidates in the
same folder as the GA. This will create a trajectory file
all_candidates.traj which includes all locally relaxed candidates
the GA has tried. This script can be run at the same time as the main
script is running. This is possible because the ase.db database
is being updated as the GA progresses.
Running the GA in Parallel¶
One of the great advantages of a GA is that many structures can be relaxed in parallel. This GA implementation includes two classes which facilitates running the GA in parallel. One class can be used for running several single threaded optimizations simultaneously on the same compute node, and the other class integrates the GA into the PBS queuing system used at many high performance computer clusters.
Relaxations in Parallel on the Same Computer¶
In order to relax several structures simultaneously on the same computer a separate script relaxing one structure needs to be created. Continuing the example from above we therefore create a script taking as input the filename of the structure to relax and which as output saves a trajectory file with the locally optimized structure. It is important that the relaxed structure is named as in this script, since the parallel integration assumes this file naming scheme. For the example described above this script could look like
from ase.optimize import BFGS
from ase.io import read, write
from ase.calculators.emt import EMT
from ase.ga.relax_attaches import VariansBreak
import sys
fname = sys.argv[1]
print('Now relaxing {0}'.format(fname))
a = read(fname)
a.calc = EMT()
dyn = BFGS(a, trajectory=None, logfile=None)
vb = VariansBreak(a, dyn)
dyn.attach(vb.write)
dyn.run(fmax=0.05)
a.info['key_value_pairs']['raw_score'] = -a.get_potential_energy()
write(fname[:-5] + '_done.traj', a)
print('Done relaxing {0}'.format(fname))
The main script needs to initialize the parallel controller and then the script needs to be changed the two places where structures are relaxed. The changed main script now looks like
from random import random
from ase.io import write
import time
from ase.ga.data import DataConnection
from ase.ga.population import Population
from ase.ga.standard_comparators import InteratomicDistanceComparator
from ase.ga.cutandsplicepairing import CutAndSplicePairing
from ase.ga.offspring_creator import OperationSelector
from ase.ga.standardmutations import MirrorMutation
from ase.ga.standardmutations import RattleMutation
from ase.ga.standardmutations import PermutationMutation
from ase.ga.utilities import closest_distances_generator
from ase.ga.utilities import get_all_atom_types
from ase.ga.parallellocalrun import ParallelLocalRun
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., 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()
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.)
# 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)
# 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.)
write('all_candidates.traj', da.get_all_relaxed_candidates())
Notice how the main script is not cluttered by the local optimization
logic and is therefore now also easier to read. n_simul controls
the number of simultaneous relaxations, and can of course also be set
to 1 effectively giving the same result as in the non parallel
situation.
The relax method on the ParallelLocalRun class only returns
control to the main script when there is an execution thread
available. In the above example the relax method immediately returns
control to the main script the first 4 times it is called, but the
fifth time control is first returned when one of the first four
relaxations have been completed.
Running the GA together with a queing system¶
The GA has been implemented with first principles structure optimization in mind. When using for instance DFT calculations for the local relaxations relaxing one structure can take many hours. For this reason the GA has been made so that it can work together with queing systems where each candidate is relaxed in a separate job. With this in mind the main script of the GA can thus also be considered a controller script which every time it is invoked gathers the current population, checks with a queing system for the number of jobs submitted, and submits new jobs. For a typical application the main script can thus be invoked by a crontab once every hour.
To run the GA together with a queing system the user needs to specify
a function which takes as input a job name and the path to the
trajectory file that needs to be submitted (the jtg function in
the sample script below). From this the function generates a PBS job
file which is submitted to the queing system. The calculator script
specified in the jobfile needs to obey the same naming scheme as the
sample calculator script in the previous section. The sample
relaxation script given in the previous can be used as starting point
for a relaxation script.
Handling of the parallel logic is in this case in the main script. The
parameter n_simul given to the PBSQueueRun object determines how
many relaxations should be in the queuing system simultaneously. The
main script now looks the following:
from random import random
from ase.io import write
from ase.ga.data import DataConnection
from ase.ga.population import Population
from ase.ga.standard_comparators import InteratomicDistanceComparator
from ase.ga.cutandsplicepairing import CutAndSplicePairing
from ase.ga.offspring_creator import OperationSelector
from ase.ga.standardmutations import MirrorMutation
from ase.ga.standardmutations import RattleMutation
from ase.ga.standardmutations import PermutationMutation
from ase.ga.utilities import closest_distances_generator
from ase.ga.utilities import get_all_atom_types
from ase.ga.pbs_queue_run import PBSQueueRun
def jtg(job_name, traj_file):
s = '#!/bin/sh\n'
s += '#PBS -l nodes=1:ppn=12\n'
s += '#PBS -l walltime=48:00:00\n'
s += '#PBS -N {0}\n'.format(job_name)
s += '#PBS -q q12\n'
s += 'cd $PBS_O_WORKDIR\n'
s += 'python calc.py {0}\n'.format(traj_file)
return s
population_size = 20
mutation_probability = 0.3
# Initialize the different components of the GA
da = DataConnection('gadb.db')
tmp_folder = 'tmp_folder/'
# The PBS queing interface is created
pbs_run = PBSQueueRun(da,
tmp_folder=tmp_folder,
job_prefix='Ag2Au2_opt',
n_simul=5,
job_template_generator=jtg)
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., 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 and
not pbs_run.enough_jobs_running()):
a = da.get_an_unrelaxed_candidate()
pbs_run.relax(a)
# create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comp)
# Submit new candidates until enough are running
while (not pbs_run.enough_jobs_running() and
len(population.get_current_population()) > 2):
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)
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
pbs_run.relax(a3)
write('all_candidates.traj', da.get_all_relaxed_candidates())
Parameterising the GA search for structure screening¶
Relaxing every candidate suggested by the GA is very inefficient. Many of these structures are poor suggestions and are immediately discarded when they are compared to the current population. For this reason it can be very effective to screen the candidate before relaxation to have a guess whether the candidate has a chance to enter the population or not. If this is not the case they can be rejected without the need for a costly DFT calculation. By doing this you could, for example, use a more drastic mutation resulting in both potentially very good but also very bad candidates without having to waste a lot of CPU power evaluating the poor suggestions.
Parameterising the whole database of structures and relating the parameters for the individual structures to their DFT energy is one example of how to handle this. As the database of structures grows doing the GA search, the fit parameters and the guessed energy becomes more refined. As a result, the screening becomes more precise.
Below is a sample script of how this method can be implemented and used. The script is a direct extension of the above tutorial. A number of predefined parameterising methods are available and its implementation is by no means restricted to the use of one of those. In the example a linear relationship is expected between every parameter and the DFT energy. The main script for the GA run hence could look like:
from random import random
from ase.io import write
from ase.ga.data import DataConnection
from ase.ga.population import Population
from ase.ga.standard_comparators import InteratomicDistanceComparator
from ase.ga.cutandsplicepairing import CutAndSplicePairing
from ase.ga.offspring_creator import OperationSelector
from ase.ga.standardmutations import MirrorMutation
from ase.ga.standardmutations import RattleMutation
from ase.ga.standardmutations import PermutationMutation
from ase.ga.utilities import closest_distances_generator
from ase.ga.utilities import get_all_atom_types
from ase.ga.pbs_queue_run import PBSQueueRun
from ase.ga import get_parametrization
import numpy as np
from ase.ga.utilities import get_atoms_connections, get_atoms_distribution
from ase.ga.utilities import get_angles_distribution
from ase.ga.utilities import get_rings, get_neighborlist
def jtg(job_name, traj_file):
s = '#!/bin/sh\n'
s += '#PBS -l nodes=1:ppn=16\n'
s += '#PBS -l walltime=100:00:00\n'
s += '#PBS -N {0}\n'.format(job_name)
s += '#PBS -q q16\n'
s += 'cd $PBS_O_WORKDIR\n'
s += 'NPROCS==`wc -l < $PBS_NODEFILE`\n'
s += 'mpirun --mca mpi_warn_on_fork 0 -np $NPROCS '
s += 'gpaw-python calc_gpaw.py {0}\n'.format(traj_file)
return s
def combine_parameters(conf):
# Get and combine selected parameters
parameters = []
gets = [get_atoms_connections(conf) + get_rings(conf) +
get_angles_distribution(conf) + get_atoms_distribution(conf)]
for get in gets:
parameters += get
return parameters
def should_we_skip(conf, comparison_energy, weights):
parameters = combine_parameters(conf)
# Return if weights not defined (too few completed
# calculated structures to make a good fit)
if weights is None:
return False
regression_energy = sum(p * q for p, q in zip(weights, parameters))
# Skip with 90% likelihood if energy appears to go up 5 eV or more
if (regression_energy - comparison_energy) > 5 and random() < 0.9:
return True
else:
return False
population_size = 20
mutation_probability = 0.3
# Initialize the different components of the GA
da = DataConnection('gadb.db')
tmp_folder = 'work_folder/'
# The PBS queing interface is created
pbs_run = PBSQueueRun(da,
tmp_folder=tmp_folder,
job_prefix='Ag2Au2_opt',
n_simul=5,
job_template_generator=jtg,
find_neighbors=get_neighborlist,
perform_parametrization=combine_parameters)
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., 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 and
not pbs_run.enough_jobs_running()):
a = da.get_an_unrelaxed_candidate()
pbs_run.relax(a)
# create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comp)
# create the regression expression for estimating the energy
all_trajs = da.get_all_relaxed_candidates()
sampled_points = []
sampled_energies = []
for conf in all_trajs:
no_of_conn = list(get_parametrization(conf))
if no_of_conn not in sampled_points:
sampled_points.append(no_of_conn)
sampled_energies.append(conf.get_potential_energy())
sampled_points = np.array(sampled_points)
sampled_energies = np.array(sampled_energies)
if len(sampled_points) > 0 and len(sampled_energies) >= len(sampled_points[0]):
weights = np.linalg.lstsq(sampled_points, sampled_energies, rcond=-1)[0]
else:
weights = None
# Submit new candidates until enough are running
while (not pbs_run.enough_jobs_running() and
len(population.get_current_population()) > 2):
a1, a2 = population.get_two_candidates()
# Selecting the "worst" parent energy
# which the child should be compared to
ce_a1 = da.get_atoms(a1.info['relax_id']).get_potential_energy()
ce_a2 = da.get_atoms(a2.info['relax_id']).get_potential_energy()
comparison_energy = min(ce_a1, ce_a2)
a3, desc = pairing.get_new_individual([a1, a2])
if a3 is None:
continue
if should_we_skip(a3, comparison_energy, weights):
continue
da.add_unrelaxed_candidate(a3, description=desc)
if random() < mutation_probability:
a3_mut, desc_mut = mutations.get_new_individual([a3])
if (a3_mut is not None and
not should_we_skip(a3_mut, comparison_energy, weights)):
da.add_unrelaxed_step(a3_mut, desc_mut)
a3 = a3_mut
pbs_run.relax(a3)
write('all_candidates.traj', da.get_all_relaxed_candidates())