def calculate_isolate_molecular_levels(self, tp):
atoms = self.isolate_atoms
atoms.pbc = True
p = tp.gpw_kwargs.copy()
p['nbands'] = None
if 'mixer' in p:
if not tp.spinpol:
p['mixer'] = Mixer(0.1, 5, weight=100.0)
else:
p['mixer'] = MixerDif(0.1, 5, weight=100.0)
p['poissonsolver'] = PoissonSolver(nn=2)
if type(p['basis']) is dict and len(p['basis']) == len(tp.atoms):
p['basis'] = 'dzp'
raise Warning('the dict basis is not surpported in isolate atoms')
if 'txt' in p and p['txt'] != '-':
p['txt'] = 'isolate_' + p['txt']
atoms.set_calculator(GPAW(**p))
atoms.get_potential_energy()
setups = atoms.calc.wfs.setups
self.project_basis_in_molecule = get_atom_indices(
self.project_atoms_in_molecule, setups)
kpt = atoms.calc.wfs.kpt_u[0]
s_mm = atoms.calc.wfs.S_qMM[0]
c_nm = eig_states_norm(kpt.C_nM, s_mm)
self.isolate_eigen_values = kpt.eps_n
self.isolate_eigen_vectors = c_nm
self.isolate_s_mm = s_mm
def calculate_isolate_molecular_levels(self, tp):
atoms = self.isolate_atoms
atoms.pbc = True
p = tp.gpw_kwargs.copy()
p['nbands'] = None
if 'mixer' in p:
if not tp.spinpol:
p['mixer'] = Mixer(0.1, 5, weight=100.0)
else:
p['mixer'] = MixerDif(0.1, 5, weight=100.0)
p['poissonsolver'] = PoissonSolver(nn=2)
if type(p['basis']) is dict and len(p['basis']) == len(tp.atoms):
p['basis'] = 'dzp'
raise Warning('the dict basis is not surpported in isolate atoms')
if 'txt' in p and p['txt'] != '-':
p['txt'] = 'isolate_' + p['txt']
atoms.set_calculator(GPAW(**p))
atoms.get_potential_energy()
setups = atoms.calc.wfs.setups
self.project_basis_in_molecule = get_atom_indices(
self.project_atoms_in_molecule, setups)
kpt = atoms.calc.wfs.kpt_u[0]
s_mm = atoms.calc.wfs.S_qMM[0]
c_nm = eig_states_norm(kpt.C_nM, s_mm)
self.isolate_eigen_values = kpt.eps_n
self.isolate_eigen_vectors = c_nm
self.isolate_s_mm = s_mm
def lead_scattering_states(self, tp, energy, l, s, q):
#Calculating the scattering states in electrodes
#l ---- index of electrode
#s ---- index of spin
#q ---- index of local k point
#if it is multi-terminal system, should add a part that can rotate
# the lead hamiltonian
MaxLambda = 1e2
MinErr = 1e-8
energy += tp.bias[l]
hes00 = tp.lead_hsd[l].H[s][q].recover() - \
tp.lead_hsd[l].S[q].recover() * energy
hes01 = tp.lead_couple_hsd[l].H[s][q].recover() - \
tp.lead_couple_hsd[l].S[q].recover() * energy
nb = hes00.shape[-1]
dtype = hes00.dtype
A = np.zeros([2 * nb, 2 * nb], dtype)
B = np.zeros([2 * nb, 2 * nb], dtype)
A[:nb, nb:2 * nb] = np.eye(nb)
A[nb:2 * nb, :nb] = hes01.T.conj()
A[nb:2 * nb, nb:2 * nb] = hes00
B[:nb, :nb] = np.eye(nb)
B[nb:2 * nb, nb:2 * nb] = -hes01
from scipy.linalg import eig
D, V = eig(A, B)
index = np.argsort(abs(D))
D = D[index]
V = V[:, index]
#delete NaN
index = find(np.abs(D) >= 0)
D = D[index]
V = V[:, index]
#delete some unreasonable solutions
index = find(abs(D) > MaxLambda)
cutlen = len(D) - index[0]
index = np.arange(cutlen, len(D) - cutlen)
D = D[index]
V = V[:, index]
k = np.log(D) * (-1.j) / 2 / np.pi
Vk = V[:nb]
#sort scattering states
proindex = find(abs(k.imag) < MinErr)
if len(proindex) > 0:
k_sort = np.sort(k[proindex].real)
index = np.argsort(k[proindex].real)
k[proindex] = k[proindex[index]].real
Vk[:, proindex] = Vk[:, proindex[index]]
#normalization the scattering states
j = proindex[0]
while j <= proindex[-1]:
same_k_index = find(abs((k - k[j]).real) < MinErr)
sk = self.lead_k_matrix(tp, l, s, q, k[j])
Vk[:, same_k_index] = eig_states_norm(Vk[:, same_k_index], sk)
j += len(same_k_index)
return np.array(k[proindex]), np.array(Vk[:, proindex])
def lead_scattering_states(self, tp, energy, l, s, q):
#Calculating the scattering states in electrodes
#l ---- index of electrode
#s ---- index of spin
#q ---- index of local k point
#if it is multi-terminal system, should add a part that can rotate
# the lead hamiltonian
MaxLambda = 1e2
MinErr = 1e-8
energy += tp.bias[l]
hes00 = tp.lead_hsd[l].H[s][q].recover() - \
tp.lead_hsd[l].S[q].recover() * energy
hes01 = tp.lead_couple_hsd[l].H[s][q].recover() - \
tp.lead_couple_hsd[l].S[q].recover() * energy
nb = hes00.shape[-1]
dtype = hes00.dtype
A = np.zeros([2*nb, 2*nb], dtype)
B = np.zeros([2*nb, 2*nb], dtype)
A[:nb, nb:2*nb] = np.eye(nb)
A[nb:2*nb, :nb] = hes01.T.conj()
A[nb:2*nb, nb:2*nb] = hes00
B[:nb, :nb] = np.eye(nb)
B[nb:2*nb, nb:2*nb] = -hes01
from scipy.linalg import eig
D, V = eig(A, B)
index = np.argsort(abs(D))
D = D[index]
V = V[:, index]
#delete NaN
index = find(np.abs(D) >= 0)
D = D[index]
V = V[:, index]
#delete some unreasonable solutions
index = find(abs(D) > MaxLambda)
cutlen = len(D) - index[0]
index = np.arange(cutlen, len(D) - cutlen)
D = D[index]
V = V[:, index]
k = np.log(D) * (-1.j) / 2 / np.pi
Vk = V[:nb]
#sort scattering states
proindex = find(abs(k.imag) < MinErr)
if len(proindex) > 0:
k_sort = np.sort(k[proindex].real)
index = np.argsort(k[proindex].real)
k[proindex] = k[proindex[index]].real
Vk[:, proindex] = Vk[:, proindex[index]]
#normalization the scattering states
j = proindex[0]
while j <= proindex[-1]:
same_k_index = find(abs((k - k[j]).real) < MinErr)
sk = self.lead_k_matrix(tp, l, s, q, k[j])
Vk[:, same_k_index] = eig_states_norm(Vk[:, same_k_index],
sk)
j += len(same_k_index)
return np.array(k[proindex]), np.array(Vk[:, proindex])