candidate_generation.py 11.8 KB
Newer Older
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
1
2
3
4
import numpy as np
from abc import ABC, abstractmethod
from ase.data import covalent_radii
from ase.geometry import get_distances
5
from ase import Atoms
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
6
7
8

from ase.visualize import view

9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
from utils import check_valid_bondlengths

class CandidateGenerator(ABC):
    """Baseclass for mutation and crossover operations as well
    as the startgenerator.

    Parameters:

    blmin: The minimum allowed distance between atoms in units of
    the covalent distance between atoms, where d_cov=r_cov_i+r_cov_j.
    
    blmax: The maximum allowed distance, in units of the covalent 
    distance, from a single isolated atom to the closest atom. If
    blmax=None, no constraint is enforced on isolated atoms.

    force_all_bonds_valid: If True all bondlengths are forced to
    be valid according to blmin/blmax. If False, only bondlengths 
    of atoms specified in bondlength checks during operations are
    tested. The specified atoms are typically the ones changed 
    during operations. Default is False, as True might cause
    problems with GOFEE, as GPR-relaxations and dual-steps might
    result in structures that does not obey blmin/blmax.
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
31
    """
32
    def __init__(self, blmin=0.7, blmax=1.4, force_all_bonds_valid=False):
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
33
34
        self.blmin = blmin
        self.blmax = blmax
35
        self.force_all_bonds_valid = force_all_bonds_valid
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
36
37
        self.description = 'Unspecified'

38
39
40
41
42
43
44
45
46
47
48
49
50
    def check_valid_bondlengths(self, a, indices=None, check_too_close=True, check_isolated=True):
        if self.force_all_bonds_valid:
            # Check all bonds (mainly for testing)
            return check_valid_bondlengths(a, self.blmin, self.blmax+0.1,
                                           check_too_close=check_too_close,
                                           check_isolated=check_isolated)
        else:
            # Check only specified ones
            # (typically only for the atoms changed during operation)
            return check_valid_bondlengths(a, self.blmin, self.blmax+0.1, indices=indices,
                                           check_too_close=check_too_close,
                                           check_isolated=check_isolated)
        
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
51
52
53
54
    @abstractmethod
    def get_new_candidate(self):
        pass

55
56
57
    def finalize(self, a, a0=None, successfull=True):
        if successfull:
            description = self.description
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
58
        else:
59
60
61
62
63
64
65
66
67
68
            description = 'failed ' + self.description
            
        try:
            a.info['key_value_pairs']['origin'] = description
        except:
            a.info['key_value_pairs'] = {'origin': description}
        if self.force_all_bonds_valid:
            # Check all bonds
            valid_bondlengths = self.check_valid_bondlengths(a)
            assert valid_bondlengths, 'bondlengths are not valid'
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
69
70
        return a

71
72
73
74
75
76

class OperationSelector():
    """Class to produce new candidates by applying one of the 
    candidate generation operations which is supplied in the
    'operations'-list. The operations are drawn randomly according
    to the 'probabilities'-list.
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
    
    operations : "list" or "list of lists" of mutations/crossovers.

    probabilities : probability for each of the mutations/crossovers
    in operations. Must have the same dimensions as operations.
    """
    def __init__(self, probabilities, operations):
        cond1 = isinstance(operations[0], list)
        cond2 = isinstance(probabilities[0], list)
        if not cond1 and not cond2:
            operations = [operations]
            probabilities = [probabilities]
        element_count_operations = [len(op_list) for op_list in operations]
        element_count_probabilities = [len(prob_list)
                                       for prob_list in probabilities]
92
        assert element_count_operations == element_count_probabilities, 'the two lists must have the same shape'
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
        self.operations = operations
        self.rho = [np.cumsum(prob_list) for prob_list in probabilities]

    def __get_index__(self, rho):
        """Draw from the cumulative probalility distribution, rho,
        to return the index of which operation to use"""
        v = np.random.random() * rho[-1]
        for i in range(len(rho)):
            if rho[i] > v:
                return i
        
    def get_new_candidate(self, parents):
        """Generate new candidate by applying a randomly drawn
        operation on the structures. This is done successively for
        each list of operations, if multiple are present.
        """
        for op_list, rho_list in zip(self.operations, self.rho):
            to_use = self.__get_index__(rho_list)
            anew = op_list[to_use].get_new_candidate(parents)
            parents[0] = anew
        return anew

115
116
117
118
119
120
121
122
123
124
125
126

def random_pos(box):
    """ Returns a random position within the box
         described by the input box. """
    p0 = box[0].astype(float)
    vspan = box[1]
    r = np.random.random((1, len(vspan)))
    pos = p0.copy()
    for i in range(len(vspan)):
        pos += vspan[i] * r[0, i]
    return pos

127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
class OperationConstraint():
    """ Class used to enforce constraints on the positions of
    atoms in mutation and crossover operations.

    Parameters:

    box: Box in which atoms are allowed to be placed. It should
    have the form [] [p0, vspan] where 'p0' is the position of
    the box corner and 'vspan' is a matrix containing the three
    spanning vectors.

    xlim: On the form [xmin, xmax], specifying, in the x-direction, 
    the lower and upper limit of the region atoms can be moved 
    within.

    ylim, zlim: See xlim.
    """
    def __init__(self, box=None, xlim=None, ylim=None, zlim=None):
        self.box = box
        self.xlim = xlim
        self.ylim = ylim
        self.zlim = zlim

    def check_if_valid(self, positions):
        """ Returns whether positions are valid under the 
        constraints or not.
        """
        if self.box is not None:
            pass
        if self.x is not None:
            if (np.any(positions[:,0] < self.xlim[0]) or 
                    np.any(positions[:,0] > self.xlim[1])):
                return False
        if self.y is not None:
            if (np.any(positions[:,1] < self.ylim[0]) or 
                    np.any(positions[:,1] > self.ylim[1])):
                return False
        if self.z is not None:
            if (np.any(positions[:,2] < self.zlim[0]) or 
                    np.any(positions[:,2] > self.zlim[1])):
                return False

169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
class StartGenerator(CandidateGenerator):
    """ Class used to generate random initial candidates.
    The candidates are generated by iteratively adding in
    one atom at a time within the box described.

    Parameters:

    slab: The atoms object describing the super cell to
    optimize within. Can be an empty cell or a cell 
    containing the atoms of a slab.

    stoichiometry: A list of atomic numbers for the atoms
    that are placed on top of the slab (if one is present).

    box_to_place_in: The box within which atoms are placed. The box
    should be on the form [p0, vspan] where 'p0' is the position of
    the box corner and 'vspan' is a matrix containing the three
    spanning vectors.

    blmin: The minimum allowed distance between atoms in units of
    the covalent distance between atoms, where d_cov=r_cov_i+r_cov_j.
    
    blmax: The maximum allowed distance, in units of the covalent 
    distance, from a single isolated atom to the closest atom. If
    blmax=None, no constraint is enforced on isolated atoms.

    cluster: If True atoms are required to be placed within
    blmin*d_cov of one of the other atoms to be placed. If
    False the atoms in the slab are also included.
    """
    def __init__(self, slab, stoichiometry, box_to_place_in,
                 blmin=0.7, blmax=1.4, cluster=False, description='StartGenerator'):
        CandidateGenerator.__init__(self, blmin=blmin, blmax=blmax)
        self.slab = slab
        self.stoichiometry = stoichiometry
        self.box = box_to_place_in
        self.cluster = cluster
        self.description = description

    def get_new_candidate(self, parents=None):
        a = self.make_structure()
        a = self.finalize(a)
        return a

    def make_structure(self):
        """ Generates a new random structure """
        Nslab = len(self.slab)
        Ntop = len(self.stoichiometry)
        num = np.random.permutation(self.stoichiometry)

        for i_trials in range(1000):
            a = self.slab.copy()
            for i in range(Ntop):
                pos_found = False
                for _ in range(300):
                    # Place new atom
                    posi = random_pos(self.box)
                    a += Atoms([num[i]], posi.reshape(1,3))

                    # Check if position of new atom is valid
                    not_too_close = self.check_valid_bondlengths(a, indices=[Nslab+i],
                                                          check_too_close=True,
                                                          check_isolated=False)
                    if len(a) == 1:  # The first atom
                        not_isolated = True
                    else:
                        if self.cluster:  # Check isolation excluding slab atoms.
                            not_isolated = self.check_valid_bondlengths(a[Nslab:], indices=[Nslab+i],
                                                                        check_too_close=False,
                                                                        check_isolated=True)
                        else:  # All atoms.
                            not_isolated = self.check_valid_bondlengths(a, indices=[Nslab+i],
                                                                        check_too_close=False,
                                                                        check_isolated=True)
                    valid_bondlengths = not_too_close and not_isolated
                    if not valid_bondlengths:
                        del a[-1]
                    else:
                        pos_found = True
                        break
                if not pos_found:
                    break
            if pos_found:
                break
        if i_trials == 999 and not pos_found:
            raise RuntimeError('StartGenerator: No valid structure was produced in 1000 trials.')
        else:
            return a
                
    
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
259
260
261
262
263
if __name__ == '__main__':
    from ase.io import read
    from ase.visualize import view

    from candidate_operations.basic_mutations import RattleMutation, RattleMutation2, PermutationMutation
264
265

    print(0.7*2*covalent_radii[1], 1.3*2*covalent_radii[1])
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
266
    
267
    np.random.seed(7)
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
268
269
270
271
    
    #a = read('/home/mkb/DFT/gpLEA/Si3x3/ref/gm_unrelaxed_done.traj', index='0')
    #a = read('si3x3.traj', index='0')
    #a = read('c6h6.traj', index='0')
272
273
274
275
276
277
278
279
280
281
    traj = read('c6h6_init.traj', index=':')
    #a = read('sn2o3.traj', index='0')
    #slab = read('slab_sn2o3.traj', index='0')

    """
    stoichiometry = 6*[50] + 10*[8]
    c = slab.get_cell()
    c[2,2] = 3.3
    p0 = np.array([0,0,14])
    box = [p0, c]
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
282
    
283
284
285
286
287
    sg = StartGenerator(slab, stoichiometry, box)
    """

    a = traj[0]
    rattle = RattleMutation(n_top=len(a), Nrattle=3, rattle_range=2)
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
288
289
290
    rattle2 = RattleMutation2(n_top=16, Nrattle=0.1)
    permut = PermutationMutation(n_top=16, Npermute=2)

291
292
    candidategenerator = OperationSelector([1], [rattle])
    #candidategenerator = CandidateGenerator([0., 1., 0.], [rattle, rattle2, permut])
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
293
294
    #candidategenerator = CandidateGenerator([[1],[1]], [[rattle2], [permut]])

295
296
297
298
299
300
301
    """
    for a in traj:
        vb = rattle.check_valid_bondlengths(a)
        print(vb)
    """

    traj_rattle = []
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
302
    for i in range(100):
303
304
305
306
307
        for j, a in enumerate(traj[13:14]):
            print('i =', i, 'j =', j)
            a0 = a.copy()
            anew = candidategenerator.get_new_candidate([a0,a0])
            traj_rattle += [a0, anew]
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
308

309
    view(traj_rattle)
Malthe Kjær Bisbo's avatar
update  
Malthe Kjær Bisbo committed
310
311
312
313
    """
    a_mut = rattle.get_new_candidate([a])
    view([a,a_mut])
    """