[ase-users] Particle_mutations in GA
Alexander Romanov
alphard.rm at gmail.com
Thu Mar 2 15:38:56 CET 2017
Sorry, it works well now.
I modified file on another computer of our cluster. It's my mistake. Thanks
for your help.
Best regards,
Alexander
2017-03-02 17:13 GMT+03:00 Alexander Romanov <alphard.rm at gmail.com>:
> Thanks for your reply Steen.
>
> We've patched particle_mutations.py script (from gitlab
> https://gitlab.com/ase/ase/merge_requests/434/diffs), but
> we still have just the same problem.
>
> Best regards,
> Alexander
>
> 2017-03-02 16:08 GMT+03:00 Steen Lysgaard <stly at dtu.dk>:
>
>> Hi Alexander,
>>
>> thanks for reporting this error.
>>
>> It should be fixed when MR !434 goes through. Specifying the number of
>> mutations (num_muts) should also work now.
>>
>> Best regards,
>>
>> Steen
>>
>> On 2017-03-02 10:51, Alexander Romanov via ase-users wrote:
>>
>> Hi,
>> We met a strange problem with RandomMutation operator in Genetic
>> Algorithm. Information about unit cell
>> is periodically missed. Length of our cell is 36 angstroms but we find 1
>> angstrom per
>> dimension in some cases
>>
>> #-----------------------------------------------------------
>> --------------------------------------------
>> 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
>> import numpy as np
>> from ase import Atoms
>> from ase.visualize import view
>> from ase.constraints import FixAtoms
>>
>> for x in range(13, 12, -1):
>> for y in range(1, 2, 1):
>> db_file = 'Cu%d_gadb_v%d.db' % (x,y,)
>>
>> L = 36.0
>>
>> cluster = Atoms('Cu',
>> cell=(L, L, L),
>> pbc=(0., 0., 0.))
>> cluster.center()
>> for a in cluster:
>> a.x=17.25
>> a.y=17.25
>> a.z=17.25
>> #constraint = FixAtoms(mask=[a.symbol == 'Cu' for a in cluster])
>> #cluster.set_constraint(constraint)
>>
>>
>> # define the volume in which cluster is optimized
>> # the volume is defined by a corner position (p0)
>> # and three spanning vectors (v1, v2, v3)
>> pos = (0.0, 0.0, 0.0)
>> b = 20.0
>> cell = np.array([(b, 0, 0), (0, b, 0), (0, 0, b)])
>> p0 = np.array([10., 10., 10.])
>> v1 = cell[0, :]
>> v2 = cell[1, :]
>> v3 = cell[2, :]
>>
>>
>> # Define the composition of the atoms to optimize
>> atom_numbers = (x-1)* [29]
>>
>>
>> # define the closest distance two atoms of a given species can be
>> to each other
>> unique_atom_types = get_all_atom_types(cluster, atom_numbers)
>> cd = closest_distances_generator(atom_numbers=unique_atom_types,
>> ratio_of_covalent_radii=0.7)
>>
>> # create the starting population
>> sg = StartGenerator(slab=cluster,
>> atom_numbers=atom_numbers,
>> closest_allowed_distances=cd,
>> 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=cluster,
>> stoichiometry=atom_numbers)
>>
>> for a in starting_population:
>> d.add_unrelaxed_candidate(a)
>>
>> #-----------------------------------------------------------
>> --------------------------------------------
>> from __future__ import print_function
>> from random import random
>> from ase.io import write
>> from ase.optimize import BFGS
>> from ase.calculators.emt import EMT
>> from ase.io import read
>>
>> 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
>> from ase.ga.particle_mutations import RandomMutation
>>
>> # Change the following three parameters to suit your needs
>> population_size = 20
>> mutation_probability = 0.3
>> n_to_test = 200
>>
>> for x in range(13, 12, -1):
>> for y in range(1, 2, 1):
>> # Initialize the different components of the GA
>> da = DataConnection('Cu%d_gadb_v%d.db' % (x,y,))
>> 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),
>> RandomMutation(length=2.,
>> num_muts=4)])
>>
>> # 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:
>> confid = a3.info.get('confid')
>> a3_mut, desc = mutations.get_new_individual([a3])
>> if a3_mut is not None:
>> a3_mut.info['confid'] = confid
>> da.add_unrelaxed_step(a3_mut, desc)
>> a3 = a3_mut
>>
>> # Relax the new candidate
>> a3.set_calculator(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_Cu%d_v%d.traj' % (x,y,),
>> da.get_all_relaxed_candidates() )
>>
>>
>> _______________________________________________
>> ase-users mailing listase-users at listserv.fysik.dtu.dkhttps://listserv.fysik.dtu.dk/mailman/listinfo/ase-users
>>
>>
>> --
>> Steen Lysgaard
>> Post doc
>> Department of Energy Conversion and Storage
>> Technical University of Denmark
>> -------------------------------------------
>> Frederiksborgvej 399, P.O. Box 49
>> 4000 Roskilde
>> Mobile +45 5194 4886stly at dtu.dkhttp://www.dtu.dk
>>
>>
>
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