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dft_tools/doc/tutorials/images_scripts/Sr2MgOsO6_noSOC.py

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# 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
# Convert the input
from triqs_dft_tools.converters.wien2k import *
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Converter = Wien2kConverter(filename = "Sr2MgOsO6_noSOC")
Converter.convert_dft_input()
# Init the SumK class:
filename = 'Sr2MgOsO6_noSOC.h5'
SK = SumkDFT(hdf_file=filename,use_dft_blocks=True)
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# 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([{'up_1': [0],'up_2': [0],'up_3': [0],'down_1': [0],'down_2': [0],'down_3': [0]}])
###########################
# Now we set up the U matrix, first in cubic Wien2k convention:
U = 2.0
J = 0.2
U_mat = U_matrix(l=2,U_int=U,J_hund=J,basis='other', T=SK.T[0].conjugate())
# Now we set up the Hamiltonian:
h_sumk = h_int_slater(['up','down'], range(5), U_mat, off_diag=True)
# And now we rotate into solver space:
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"] = 1000000
# tail fit
p["perform_tail_fit"] = True
p["fit_max_moment"] = 4
p["fit_min_n"] = 30
p["fit_max_n"] = 70
# 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.symm_deg_gf(S.Sigma_iw) # symmetrizing Sigma
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')['up_1'][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)
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# Solve the impurity problem:
S.solve(h_int=h_int, **p)
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# 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)
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# 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
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# 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)
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# Save stuff into the user_data group of hdf5 archive in case of rerun:
SK.save(['chemical_potential','dc_imp','dc_energ'])
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