mirror of
https://github.com/triqs/dft_tools
synced 2024-12-23 12:55:17 +01:00
f27399758e
merged unstable into py3
* include the major changes of a94f8ed942
of blockstructure.py
* adapted all code to python 3
* fixed all tests
113 lines
3.8 KiB
Python
113 lines
3.8 KiB
Python
# Import the modules:
|
|
from triqs_dft_tools.sumk_dft import *
|
|
from triqs.gf import *
|
|
from h5 import HDFArchive
|
|
from triqs.operators.util import *
|
|
from triqs.operators.util.U_matrix import *
|
|
from triqs_cthyb import *
|
|
import triqs.utility.mpi as mpi
|
|
|
|
# Init the SumK class:
|
|
filename = 'Sr2MgOsO6_SOC.h5'
|
|
SK = SumkDFT(hdf_file=filename,use_dft_blocks=True)
|
|
|
|
# Find diagonal local basis set:
|
|
SK.calculate_diagonalization_matrix(prop_to_be_diagonal='eal',calc_in_solver_blocks=True)
|
|
|
|
###########################
|
|
# Now we pick the orbitals:
|
|
# BE CAREFUL: THIS NEEDS TO BE DONE PROPERLY
|
|
# AND IS DIFFERENT FORM CASE TO CASE!
|
|
SK.block_structure.pick_gf_struct_solver([{'ud_0': [0,1,2],'ud_1': [0,1,2]}])
|
|
###########################
|
|
|
|
# Now we set up the U matrix, first in cubic (Wien2k) convention:
|
|
U = 2.0
|
|
J = 0.2
|
|
U_sph = U_matrix(l=2, U_int=U, J_hund=J)
|
|
U_sph = np.kron(np.reshape(np.eye(2),(1,2,1,2)),np.kron(np.reshape(np.eye(2),(2,1,2,1)),U_sph))
|
|
U_mat = transform_U_matrix(U_sph, SK.T[0].conjugate())
|
|
|
|
# Now we set up the interaction Hamiltonian
|
|
h_sumk = h_int_slater(['ud'], range(10), U_mat, off_diag=True, complex=True)
|
|
# And convert it to the solver structure
|
|
h_int = SK.block_structure.convert_operator(h_sumk)
|
|
|
|
# Solver Init:
|
|
beta = 40.0
|
|
S = Solver(beta=beta, gf_struct=SK.block_structure.gf_struct_solver_list[0])
|
|
|
|
# Solver parameters:
|
|
p = {}
|
|
# solver
|
|
p["random_seed"] = 123 * mpi.rank + 567
|
|
p["length_cycle"] = 200
|
|
p["n_warmup_cycles"] = 100000
|
|
p["n_cycles"] = 3000000
|
|
# tail fit
|
|
p["perform_tail_fit"] = True
|
|
p["fit_max_moment"] = 4
|
|
p["fit_min_w"] = 4.0
|
|
p["fit_max_w"] = 8.0
|
|
|
|
# double counting correction:
|
|
dc_type = 0 # FLL
|
|
# DMFT loops:
|
|
n_loops = 1
|
|
|
|
#for first iteration, add the outout group:
|
|
if mpi.is_master_node():
|
|
with HDFArchive(filename) as ar:
|
|
if (not ar.is_group('dmft_output')):
|
|
ar.create_group('dmft_output')
|
|
|
|
for iteration_number in range(1,n_loops+1):
|
|
|
|
mpi.report("Iteration = %s"%iteration_number)
|
|
|
|
SK.set_Sigma([ S.Sigma_iw ]) # put Sigma into the SumK class
|
|
chemical_potential = SK.calc_mu( precision = 0.01 ) # find the chemical potential for given density
|
|
S.G_iw << SK.extract_G_loc()[0]
|
|
|
|
if (iteration_number==1):
|
|
# Put Hartree energy on Re Sigma
|
|
dm = S.G_iw.density()
|
|
SK.calc_dc(dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type)
|
|
S.Sigma_iw << SK.block_structure.convert_matrix(SK.dc_imp[0],space_from='sumk',space_to='solver')['ud_0'][0,0]
|
|
|
|
mpi.report("Orbital densities of local Green function :")
|
|
for s,gf in S.G_iw:
|
|
mpi.report("Orbital %s: %s"%(s,dm[s].real))
|
|
mpi.report("Total charge of Gloc : %.6f"%S.G_iw.total_density().real)
|
|
|
|
# Calculate new G0_iw to input into the solver:
|
|
S.G0_iw << S.Sigma_iw + inverse(S.G_iw)
|
|
S.G0_iw << inverse(S.G0_iw)
|
|
|
|
# Solve the impurity problem:
|
|
S.solve(h_int=h_int, **p)
|
|
|
|
# Solved. Now do post-solution stuff:
|
|
dm = S.G_iw.density()
|
|
mpi.report("Orbital densities of impurity Green function:")
|
|
for s,gf in S.G_iw:
|
|
mpi.report("Orbital %s: %s"%(s,dm[s].real))
|
|
mpi.report("Total charge of impurity problem : %.6f"%S.G_iw.total_density().real)
|
|
|
|
# Write the final Sigma and G to the hdf5 archive:
|
|
if mpi.is_master_node():
|
|
with HDFArchive(filename) as ar:
|
|
ar['dmft_output']['iterations'] = iteration_number
|
|
ar['dmft_output']['G_0'] = S.G0_iw
|
|
ar['dmft_output']['G_tau'] = S.G_tau
|
|
ar['dmft_output']['G_iw'] = S.G_iw
|
|
ar['dmft_output']['Sigma_iw_%s'%iteration_number] = S.Sigma_iw
|
|
|
|
# Set the new double counting:
|
|
dm = S.G_iw.density() # compute the density matrix of the impurity problem
|
|
SK.calc_dc(dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type)
|
|
|
|
# Save stuff into the user_data group of hdf5 archive in case of rerun:
|
|
SK.save(['chemical_potential','dc_imp','dc_energ'])
|
|
|