def run_ga(n_to_test): """ This method specifies how to run the GA once the initial random structures have been stored in godb.db. """ # Various initializations: population_size = 10 # maximal size of the population da = DataConnection('godb.db') atom_numbers_to_optimize = da.get_atom_numbers_to_optimize() # = [14] * 7 n_to_optimize = len(atom_numbers_to_optimize) # = 7 # This defines how close the Si atoms are allowed to get # in candidate structures generated by the genetic operators: blmin = closest_distances_generator(atom_numbers_to_optimize, ratio_of_covalent_radii=0.4) # This is our OFPComparator instance which will be # used to judge whether or not two structures are identical: comparator = OFPComparator(n_top=None, dE=1.0, cos_dist_max=1e-3, rcut=10., binwidth=0.05, pbc=[False]*3, sigma=0.1, nsigma=4, recalculate=False) # Defining a typical combination of genetic operators: pairing = CutAndSplicePairing(da.get_slab(), n_to_optimize, blmin) rattlemut = RattleMutation(blmin, n_to_optimize, rattle_prop=0.8, rattle_strength=1.5) operators = OperationSelector([2., 1.], [pairing, rattlemut]) # Relax the randomly generated initial candidates: while da.get_number_of_unrelaxed_candidates() > 0: a = da.get_an_unrelaxed_candidate() a = relax_one(a) da.add_relaxed_step(a) # Create the population population = Population(data_connection=da, population_size=population_size, comparator=comparator, logfile='log.txt') current_pop = population.get_current_population() # Test n_to_test new candidates for step in range(n_to_test): print('Starting configuration number %d' % step, flush=True) a3 = None while a3 is None: a1, a2 = population.get_two_candidates() a3, description = operators.get_new_individual([a1, a2]) da.add_unrelaxed_candidate(a3, description=description) a3 = relax_one(a3) da.add_relaxed_step(a3) population.update() best = population.get_current_population()[0] print('Highest raw score at this point: %.3f' % get_raw_score(best)) print('GA finished after step %d' % step) write('all_candidates.traj', da.get_all_relaxed_candidates()) write('current_population.traj', population.get_current_population())
def ga_init(self, ddiff, dmax, dE): self.ddiff = ddiff self.dmax = dmax self.dE = dE da = DataConnection(self.db_name) atom_numbers_to_optimize = da.get_atom_numbers_to_optimize( ) # adsorbate atom numbers to optimize n_to_optimize = len( atom_numbers_to_optimize) # number of atoms 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=.7) # closest distance atoms can be comp = InteratomicDistanceComparator( n_top=None, pair_cor_cum_diff=self.ddiff, pair_cor_max=dmax, dE=dE, mic=True) # comparator to determine if parents should make childer pairing = CutAndSplicePairing( blmin, None, use_tags=True, p1=.2 ) # how children are generated (make sure your adsorbates are uniquely tagged) population = Population(data_connection=da, population_size=self.pop, comparator=comp) for i in range(self.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]) #print(a3.info) #view(a3) if a3 is None: continue da.add_unrelaxed_candidate(a3, description=desc)
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) 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:
[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() 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) a.set_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 xrange(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 == 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 != None: da.add_unrelaxed_step(a3_mut, desc)
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():
strainmut, rotmut, rattlerotmut, softmut]) # Relaxing the initial candidates while da.get_number_of_unrelaxed_candidates() > 0: a = da.get_an_unrelaxed_candidate() relax(a) da.add_relaxed_step(a) # The structure comparator for the population comp = OFPComparator(n_top=n_top, dE=1.0, cos_dist_max=5e-3, rcut=10., binwidth=0.05, pbc=[True, True, True], sigma=0.05, nsigma=4, recalculate=False) # The population population = Population(data_connection=da, population_size=10, comparator=comp, logfile='log.txt') current_pop = population.get_current_population() strainmut.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4) pairing.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4) # Test a few new candidates n_to_test = 10 for step in range(n_to_test): print('Now starting configuration number {0}'.format(step), flush=True) # Generate a new candidate a3 = None while a3 is None:
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)
def run_ga(n_to_test, kptdensity=3.5): population_size = 20 da = DataConnection('godb.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, 0.05) # 0.5 # defining genetic operators: mutation_probability = 0.75 pairing = CutAndSplicePairing(blmin, p1=1., p2=0., minfrac=0.15, use_tags=False) cellbounds = CellBounds( bounds={ 'phi': [0.2 * 180., 0.8 * 180.], 'chi': [0.2 * 180., 0.8 * 180.], 'psi': [0.2 * 180., 0.8 * 180.] }) strainmut = StrainMutation(blmin, stddev=0.7, cellbounds=cellbounds, use_tags=False) blmin_soft = closest_distances_generator(all_atom_types, 0.1) softmut = SoftMutation(blmin_soft, bounds=[2., 5.], use_tags=False) rattlemut = RattleMutation(blmin, n_to_optimize, rattle_prop=0.8, rattle_strength=2.5, use_tags=False) mutations = OperationSelector([4., 4., 2], [softmut, strainmut, rattlemut]) if True: # recalculate raw scores structures = da.get_all_relaxed_candidates() for atoms in structures: atoms = singlepoint(atoms, kptdensity=kptdensity) da.c.delete([atoms.info['relax_id']]) if 'data' not in atoms.info: atoms.info['data'] = {} da.add_relaxed_step(atoms) print('Finished recalculating raw scores') # relaxing the initial candidates: while da.get_number_of_unrelaxed_candidates() > 0: a = da.get_an_unrelaxed_candidate() a.wrap() a = relax_one(a, kptdensity=kptdensity) da.add_relaxed_step(a) # create the population population = Population(data_connection=da, population_size=population_size, comparator=comparator, logfile='log.txt') current_pop = population.get_current_population() strainmut.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4) pairing.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4) # Test n_to_test new candidates ga_raw_scores = [] step = 0 for step in range(n_to_test): print('Starting configuration number %d' % step, flush=True) clock = time() a3 = None r = random() if r > mutation_probability: while a3 is None: a1, a2 = population.get_two_candidates() a3, desc = pairing.get_new_individual([a1, a2]) else: while a3 is None: a1 = population.get_one_candidate() a3, desc = mutations.get_new_individual([a1]) dt = time() - clock op = 'pairing' if r > mutation_probability else 'mutating' print('Time for %s candidate(s): %.3f' % (op, dt), flush=True) a3.wrap() da.add_unrelaxed_candidate(a3, description=desc) a3 = relax_one(a3, kptdensity=kptdensity) da.add_relaxed_step(a3) # Various updates: population.update() current_pop = population.get_current_population() if step % 10 == 0: strainmut.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4) pairing.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4) write('current_population.traj', current_pop) # Print out information for easy analysis/plotting afterwards: if r > mutation_probability: print('Step %d %s %.3f %.3f %.3f' % (step, desc,\ get_raw_score(a1), get_raw_score(a2), get_raw_score(a3))) else: print('Step %d %s %.3f %.3f' % (step, desc,\ get_raw_score(a1), get_raw_score(a3))) print('Step %d highest raw score in pop: %.3f' % \ (step, get_raw_score(current_pop[0]))) ga_raw_scores.append(get_raw_score(a3)) print('Step %d highest raw score generated by GA: %.3f' % \ (step, max(ga_raw_scores))) emin = population.pop[0].get_potential_energy() print('GA finished after step %d' % step) print('Lowest energy = %8.3f eV' % emin, flush=True) write('all_candidates.traj', da.get_all_relaxed_candidates()) write('current_population.traj', population.get_current_population())
def run_ga(n_to_test, kptdensity=None): ''' This method specifies how to run the GA once the initial random structures have been stored in godb.db. ''' # Various initializations: population_size = 10 da = DataConnection('godb.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=0.05) # Defining the mix of genetic operators: mutation_probability = 0.3333 pairing = CutAndSplicePairing(slab, n_to_optimize, blmin) rattlemut = RattleMutation(blmin, n_to_optimize, rattle_prop=0.8, rattle_strength=1.5) mirrormut = MirrorMutation(blmin, n_to_optimize) mutations = OperationSelector([1., 1.], [rattlemut, mirrormut]) if True: # Recalculate raw scores of any relaxed candidates # present in the godb.db database (only applies to # iter007). structures = da.get_all_relaxed_candidates() for atoms in structures: atoms = singlepoint(atoms) da.c.delete([atoms.info['relax_id']]) if 'data' not in atoms.info: atoms.info['data'] = {} da.add_relaxed_step(atoms) print('Finished recalculating raw scores') # Relax the randomly generated initial candidates: while da.get_number_of_unrelaxed_candidates() > 0: a = da.get_an_unrelaxed_candidate() a.wrap() a = relax_one(a) da.add_relaxed_step(a) # Create the population population = Population(data_connection=da, population_size=population_size, comparator=comparator, logfile='log.txt') current_pop = population.get_current_population() # Test n_to_test new candidates ga_raw_scores = [] step = 0 for step in range(n_to_test): print('Starting configuration number %d' % step, flush=True) clock = time() a3 = None r = random() if r > mutation_probability: while a3 is None: a1, a2 = population.get_two_candidates() a3, desc = pairing.get_new_individual([a1, a2]) else: while a3 is None: a1 = population.get_one_candidate() a3, desc = mutations.get_new_individual([a1]) dt = time() - clock op = 'pairing' if r > mutation_probability else 'mutating' print('Time for %s candidate(s): %.3f' % (op, dt), flush=True) a3.wrap() da.add_unrelaxed_candidate(a3, description=desc) a3 = relax_one(a3) da.add_relaxed_step(a3) # Various updates: population.update() current_pop = population.get_current_population() write('current_population.traj', current_pop) # Print out information for easy analysis/plotting afterwards: if r > mutation_probability: print('Step %d %s %.3f %.3f %.3f' % (step, desc,\ get_raw_score(a1), get_raw_score(a2), get_raw_score(a3))) else: print('Step %d %s %.3f %.3f' % (step, desc,\ get_raw_score(a1), get_raw_score(a3))) print('Step %d highest raw score in pop: %.3f' % \ (step, get_raw_score(current_pop[0]))) ga_raw_scores.append(get_raw_score(a3)) print('Step %d highest raw score generated by GA: %.3f' % \ (step, max(ga_raw_scores))) emin = population.pop[0].get_potential_energy() print('GA finished after step %d' % step) print('Lowest energy = %8.3f eV' % emin, flush=True) write('all_candidates.traj', da.get_all_relaxed_candidates()) write('current_population.traj', population.get_current_population())
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())
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 xrange(num_gens): if cc.converged():
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)