mirror of
https://github.com/triqs/dft_tools
synced 2024-06-23 05:32:17 +02:00
abd087e674
* Replace <<= with << * Reordering in put_Sigma * Remove all instances of wien2triqs in doc
142 lines
5.3 KiB
Python
142 lines
5.3 KiB
Python
import numpy as np
|
|
import time
|
|
import os
|
|
import pytriqs.utility.mpi as mpi
|
|
from itertools import *
|
|
from pytriqs.parameters.parameters import Parameters
|
|
from pytriqs.operators.operators2 import *
|
|
from pytriqs.archive import HDFArchive
|
|
from pytriqs.applications.impurity_solvers.cthyb import *
|
|
from pytriqs.gf.local import *
|
|
from pytriqs.applications.dft.sumk_lda import *
|
|
from pytriqs.applications.dft.converters.wien2k_converter import *
|
|
from pytriqs.applications.dft.solver_multiband import *
|
|
|
|
lda_filename='Gd_fcc'
|
|
U = 9.6
|
|
J = 0.8
|
|
beta = 40
|
|
loops = 10 # Number of DMFT sc-loops
|
|
sigma_mix = 1.0 # Mixing factor of Sigma after solution of the AIM
|
|
delta_mix = 1.0 # Mixing factor of Delta as input for the AIM
|
|
dc_type = 0 # DC type: 0 FLL, 1 Held, 2 AMF
|
|
use_blocks = True # use bloc structure from LDA input
|
|
prec_mu = 0.0001
|
|
|
|
# Solver parameters
|
|
p = {}
|
|
p["max_time"] = -1
|
|
p["random_name"] = ""
|
|
p["random_seed"] = 123 * mpi.rank + 567
|
|
p["verbosity"] = 3
|
|
p["length_cycle"] = 50
|
|
p["n_warmup_cycles"] = 50
|
|
p["n_cycles"] = 5000
|
|
|
|
Converter = Wien2kConverter(filename=lda_filename, repacking=True)
|
|
Converter.convert_dmft_input()
|
|
mpi.barrier()
|
|
|
|
previous_runs = 0
|
|
previous_present = False
|
|
if mpi.is_master_node():
|
|
ar = HDFArchive(lda_filename+'.h5','a')
|
|
if 'iterations' in ar:
|
|
previous_present = True
|
|
previous_runs = ar['iterations']
|
|
del ar
|
|
previous_runs = mpi.bcast(previous_runs)
|
|
previous_present = mpi.bcast(previous_present)
|
|
# if previous runs are present, no need for recalculating the bloc structure:
|
|
calc_blocs = use_blocks and (not previous_present)
|
|
|
|
SK=SumkLDA(hdf_file=lda_filename+'.h5',use_lda_blocks=calc_blocs)
|
|
|
|
n_orb = SK.corr_shells[0][3]
|
|
l = SK.corr_shells[0][2]
|
|
spin_names = ["up","down"]
|
|
orb_names = ["%s"%i for i in range(num_orbitals)]
|
|
orb_hybridized = False
|
|
|
|
# Construct U matrix for density-density calculations
|
|
gf_struct = set_operator_structure(spin_names,orb_names,orb_hybridized)
|
|
# Construct U matrix for density-density calculations
|
|
Umat, Upmat = U_matrix_kanamori(n_orb=n_orb, U_int=U, J_hund=J)
|
|
# Construct Hamiltonian and solver
|
|
H = h_loc_density(spin_names, orb_names, orb_hybridized, U=Umat, Uprime=Upmat, H_dump="H.txt")
|
|
S = Solver(beta=beta, gf_struct=gf_struct)
|
|
|
|
if (previous_present):
|
|
if (mpi.is_master_node()):
|
|
ar = HDFArchive(lda_filename+'.h5','a')
|
|
S.Sigma_iw << ar['Sigma_iw']
|
|
del ar
|
|
S.Sigma_iw = mpi.bcast(S.Sigma_iw)
|
|
|
|
for iteration_number in range(1,loops+1):
|
|
|
|
SK.symm_deg_gf(S.Sigma_iw,orb=0) # symmetrise Sigma
|
|
SK.put_Sigma(Sigma_imp = [ S.Sigma_iw ]) # put Sigma into the SumK class
|
|
chemical_potential = SK.find_mu( precision = prec_mu ) # find the chemical potential for the given density
|
|
S.G_iw << SK.extract_G_loc()[0] # extract the local Green function
|
|
mpi.report("Total charge of Gloc : %.6f"%S.G_iw.total_density())
|
|
|
|
if ((iteration_number==1)and(previous_present==False)):
|
|
# Init the DC term and the real part of Sigma, if no previous run was found:
|
|
dm = S.G_iw.density()
|
|
SK.set_dc(dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type)
|
|
S.Sigma_iw << SK.dc_imp[0]['up'][0,0]
|
|
|
|
# now calculate new G0_iw to input into the solver:
|
|
if (mpi.is_master_node()):
|
|
# We can do a mixing of Delta in order to stabilize the DMFT iterations:
|
|
S.G0_iw << S.Sigma_iw + inverse(S.G_iw)
|
|
ar = HDFArchive(lda_filename+'.h5','a')
|
|
if ((iteration_number>1) or (previous_present)):
|
|
mpi.report("Mixing input Delta with factor %s"%delta_mix)
|
|
Delta = (delta_mix * S.G0_iw.delta()) + (1.0-delta_mix) * ar['Delta_iw']
|
|
S.G0_iw << S.G0_iw + S.G0_iw.delta() - Delta
|
|
|
|
ar['Delta_iw'] = S.G0_iw.delta()
|
|
S.G0_iw << inverse(S.G0_iw)
|
|
del ar
|
|
|
|
S.G0_iw = mpi.bcast(S.G0_iw)
|
|
|
|
# Solve the impurity problem:
|
|
S.solve(h_loc=h_loc, **p)
|
|
|
|
# solution done, do the post-processing:
|
|
mpi.report("Total charge of impurity problem : %.6f"%S.G_iw.total_density())
|
|
|
|
# Now mix Sigma and G with factor sigma_mix, if wanted:
|
|
if ((iteration_number>1) or (previous_present)):
|
|
if (mpi.is_master_node()):
|
|
ar = HDFArchive(lda_filename+'.h5','a')
|
|
mpi.report("Mixing Sigma and G with factor %s"%sigma_mix)
|
|
S.Sigma_iw << sigma_mix * S.Sigma_iw + (1.0-sigma_mix) * ar['Sigma_iw']
|
|
S.G_iw << sigma_mix * S.G_iw + (1.0-sigma_mix) * ar['G_iw']
|
|
del ar
|
|
S.G_iw = mpi.bcast(S.G_iw)
|
|
S.Sigma_iw = mpi.bcast(S.Sigma_iw)
|
|
|
|
# Write the final Sigma and G to the hdf5 archive:
|
|
if (mpi.is_master_node()):
|
|
ar = HDFArchive(lda_filename+'.h5','a')
|
|
ar['iterations'] = previous_runs + iteration_number
|
|
ar['Sigma_iw'] = S.Sigma_iw
|
|
ar['G_iw'] = S.G_iw
|
|
del ar
|
|
|
|
dm = S.G_iw.density() # compute the density matrix of the impurity problem
|
|
# Set the double counting
|
|
SK.set_dc( dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type)
|
|
|
|
# Save stuff into the hdf5 archive:
|
|
SK.save()
|
|
|
|
if mpi.is_master_node():
|
|
ar = HDFArchive("ldadmft.h5",'w')
|
|
ar["G_iw"] = S.G_iw
|
|
ar["Sigma_iw"] = S.Sigma_iw
|