def relax(input_atoms, ref_db):
atoms_string = input_atoms.get_chemical_symbols()
# Open connection to the database with reference data
db = connect(ref_db)
# Load our model structure which is just FCC
atoms = FaceCenteredCubic('X', latticeconstant=1.)
atoms.set_chemical_symbols(atoms_string)
# Compute the average lattice constant of the metals in this individual
# and the sum of energies of the constituent metals in the fcc lattice
# we will need this for calculating the heat of formation
a = 0
ei = 0
for m in set(atoms_string):
dct = db.get(metal=m)
count = atoms_string.count(m)
a += count * dct.latticeconstant
ei += count * dct.energy_per_atom
a /= len(atoms_string)
atoms.set_cell([a, a, a], scale_atoms=True)
# Since calculations are extremely fast with EMT we can also do a volume
# relaxation
atoms.set_calculator(EMT())
eps = 0.05
volumes = (a * np.linspace(1 - eps, 1 + eps, 9))**3
energies = []
for v in volumes:
atoms.set_cell([v**(1. / 3)] * 3, scale_atoms=True)
energies.append(atoms.get_potential_energy())
eos = EquationOfState(volumes, energies)
v1, ef, B = eos.fit()
latticeconstant = v1**(1. / 3)
# Calculate the heat of formation by subtracting ef with ei
hof = (ef - ei) / len(atoms)
# Place the calculated parameters in the info dictionary of the
# input_atoms object
input_atoms.info['key_value_pairs']['hof'] = hof
# Raw score must always be set
# Use one of the following two; they are equivalent
input_atoms.info['key_value_pairs']['raw_score'] = -hof
# set_raw_score(input_atoms, -hof)
input_atoms.info['key_value_pairs']['latticeconstant'] = latticeconstant
# Setting the atoms_string directly for easier analysis
atoms_string = ''.join(input_atoms.get_chemical_symbols())
input_atoms.info['key_value_pairs']['atoms_string'] = atoms_string
def relax(input_atoms, ref_db):
atoms_string = input_atoms.get_chemical_symbols()
# Open connection to the database with reference data
db = connect(ref_db)
# Load our model structure which is just FCC
atoms = FaceCenteredCubic('X', latticeconstant=1.)
atoms.set_chemical_symbols(atoms_string)
# Compute the average lattice constant of the metals in this individual
# and the sum of energies of the constituent metals in the fcc lattice
# we will need this for calculating the heat of formation
a = 0
ei = 0
for m in set(atoms_string):
dct = db.get(metal=m)
count = atoms_string.count(m)
a += count * dct.latticeconstant
ei += count * dct.energy_per_atom
a /= len(atoms_string)
atoms.set_cell([a, a, a], scale_atoms=True)
# Since calculations are extremely fast with EMT we can also do a volume
# relaxation
atoms.set_calculator(EMT())
eps = 0.05
volumes = (a * np.linspace(1 - eps, 1 + eps, 9))**3
energies = []
for v in volumes:
atoms.set_cell([v**(1. / 3)] * 3, scale_atoms=True)
energies.append(atoms.get_potential_energy())
eos = EquationOfState(volumes, energies)
v1, ef, B = eos.fit()
latticeconstant = v1**(1. / 3)
# Calculate the heat of formation by subtracting ef with ei
hof = (ef - ei) / len(atoms)
# Place the calculated parameters in the info dictionary of the
# input_atoms object
input_atoms.info['key_value_pairs']['hof'] = hof
# Raw score must always be set
# Use one of the following two; they are equivalent
input_atoms.info['key_value_pairs']['raw_score'] = -hof
# set_raw_score(input_atoms, -hof)
input_atoms.info['key_value_pairs']['latticeconstant'] = latticeconstant
# Setting the atoms_string directly for easier analysis
atoms_string = ''.join(input_atoms.get_chemical_symbols())
input_atoms.info['key_value_pairs']['atoms_string'] = atoms_string
def phonon_unfold():
atoms = FaceCenteredCubic(size=(1, 1, 1), symbol="Cu", pbc=True)
symbols = atoms.get_chemical_symbols()
#symbols[-1] = 'Ag'
atoms.set_chemical_symbols(symbols)
calc = EMT()
atoms.set_calculator(calc)
phonon = calculate_phonon(atoms,
calc,
ndim=np.eye(3) * 2,
primitive_matrix=np.eye(3) / 1.0)
kpts, x, X, names = kpath()
kpts = [
np.dot(
k,
np.linalg.inv((np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]]) / 2.0)))
for k in kpts
]
phonon.set_qpoints_phonon(kpts, is_eigenvectors=True)
freqs, eigvecs = phonon.get_qpoints_phonon()
sc = atoms
sc_mat = np.linalg.inv((np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]]) / 2.0))
spos = sc.get_scaled_positions()
uf = phonon_unfolder(atoms, sc_mat, eigvecs, kpts)
weights = uf.get_weights()
ax = None
ax = plot_band_weight([list(x)] * freqs.shape[1],
freqs.T * 33.356,
weights[:, :].T * 0.98 + 0.01,
xticks=[names, X],
axis=ax)
# print freqs
# for i in range(freqs.shape[1]):
# plt.plot(x, freqs[:, i], color='blue', alpha=0.2, linewidth=2.5)
plt.xticks(X, names)
plt.ylabel('Frequency (cm$^{-1}$)')
plt.ylim([0, 350])
plt.title('with defect (1/4) (method: reciprocal)')
get_phonon_prim(ax)
plt.savefig('defrec4.png')
plt.show()
