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Priyanka Seth 2014-09-22 19:31:02 +02:00
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lda_dmft_cthyb.py Normal file
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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 = SolverCore.solve_parameters()
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
Umat, Upmat = U_matrix_kanamori(n_orb=n_orb, U_int=U, J_hund=J)
# Construct Hamiltonian and solver
L = LocalProblem(spin_names, orb_names, orb_hybridized, h_loc_type="density", U=Umat, Uprime=Upmat, H_dump="H.txt")
S = Solver(beta=beta, gf_struct=L.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=L.h_loc, params=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