def get_mutant(self, cluster):
cluster.sort_type()
atom_types = list(cluster.atom_types)
swap0 = np.random.randint(0, get_composition(atom_types)[0])
swap1 = np.random.randint(
get_composition(atom_types)[0], cluster.natoms)
atom_types[swap0] = 1
atom_types[swap1] = 0
mutant = Cluster(cluster.natoms,
np.array(cluster._coords),
cluster.minimiser,
atom_types=atom_types,
labels=cluster.labels)
mutant.sort_type()
return mutant
def get_offspring(self, cluster_a, cluster_b):
'''Generate an offspring structure from two parent structures.'''
#Prepare clusters
for cluster in [cluster_a, cluster_b]:
cluster.centre()
cluster.rotate_random()
cluster.sort_z()
#Choose cutting plane
cut = np.random.randint(1, cluster_a.natoms)
#Make new cluster
coords = np.empty(shape=(cluster_a.natoms, 3))
atom_types = []
for i in range(0, cut):
coords[i] = cluster_a.get_coords(i)
atom_types.append(cluster_a.atom_types[i])
for i in range(cut, cluster_a.natoms):
coords[i] = cluster_b.get_coords(i)
atom_types.append(cluster_b.atom_types[i])
composition = get_composition(cluster_a.atom_types)
atom_types = fix_composition(composition, atom_types)
offspring = Cluster(cluster_a.natoms,
coords,
cluster_a.minimiser,
atom_types=atom_types,
labels=cluster_a.labels)
offspring.quenched = False
offspring.sort_type()
cluster_a.sort_type()
cluster_b.sort_type()
return offspring
def minimise (self,cluster):
'''Minimise a cluster
parameters:
cluster- a Cluster object from bcga.cluster
Using this method will overwrite the coordinates and energy of the
supplied Cluster object.'''
# Fix overlapping atoms to avoid NWChem errors
cluster.fix_overlaps(1.5)
# Set up element labels for NWChem
atom_string=""
for i in range(0,len(cluster.labels)):
atom_string+=cluster.labels[i]+str(get_composition(cluster.atom_types)[i])
print(atom_string)
mol = Atoms(atom_string,
positions=cluster._coords,
cell=(6.0,6.0,6.0))
mol.center()
# Run GPAW calculation
calc = GPAW(**self.GPAWargs)
mol.set_calculator(calc)
opt = BFGSLineSearch(mol)
try:
opt.run(fmax=0.25)
except:
sys.exit()
# Get back cluster properties from GPAW
cluster.energy=mol.get_potential_energy()
cluster.quenched=True
return cluster
def get_offspring(self,cluster_a,cluster_b):
'''Generate an offspring structure from two parent structures.'''
#Prepare clusters
for cluster in [cluster_a,cluster_b]:
cluster.centre()
cluster.rotate_random()
cluster.sort_z()
#Choose cutting plane
cut=np.random.randint(1,cluster_a.natoms)
#Make new cluster
coords=np.empty(shape=(cluster_a.natoms,3))
atom_types=[]
for i in range(0,cut):
coords[i]=cluster_a.get_coords(i)
atom_types.append(cluster_a.atom_types[i])
for i in range(cut,cluster_a.natoms):
coords[i]=cluster_b.get_coords(i)
atom_types.append(cluster_b.atom_types[i])
composition = get_composition(cluster_a.atom_types)
atom_types=fix_composition(composition,atom_types)
offspring=Cluster(cluster_a.natoms,
coords,
cluster_a.minimiser,
atom_types=atom_types,
labels=cluster_a.labels)
offspring.quenched=False
offspring.sort_type()
cluster_a.sort_type()
cluster_b.sort_type()
return offspring
def minimise(self, cluster):
'''Minimise a cluster
parameters:
cluster- a Cluster object from bcga.cluster
Using this method will overwrite the coordinates and energy of the
supplied Cluster object.'''
# Fix overlapping atoms to avoid NWChem errors
cluster.fix_overlaps(1.5)
# Set up element labels for NWChem
atom_string = ""
for i in range(0, len(cluster.labels)):
atom_string += cluster.labels[i] + str(
get_composition(cluster.atom_types)[i])
print(atom_string)
mol = Atoms(atom_string,
positions=cluster._coords,
cell=(6.0, 6.0, 6.0))
mol.center()
# Run GPAW calculation
calc = GPAW(**self.GPAWargs)
mol.set_calculator(calc)
opt = BFGSLineSearch(mol)
try:
opt.run(fmax=0.25)
except:
sys.exit()
# Get back cluster properties from GPAW
cluster.energy = mol.get_potential_energy()
cluster.quenched = True
return cluster
def get_mutant(self,cluster):
cluster.sort_type()
atom_types=list(cluster.atom_types)
swap0=np.random.randint(0,
get_composition(atom_types)[0])
swap1=np.random.randint(get_composition(atom_types)[0],
cluster.natoms)
atom_types[swap0]=1
atom_types[swap1]=0
mutant=Cluster(cluster.natoms,
np.array(cluster._coords),
cluster.minimiser,
atom_types=atom_types,
labels=cluster.labels)
mutant.sort_type()
return mutant
