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dft_dmft_cthyb.py
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dft_dmft_cthyb.py
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import pytriqs.utility.mpi as mpi
from pytriqs.operators.util import *
from pytriqs.archive import HDFArchive
from triqs_cthyb import *
from pytriqs.gf import *
from triqs_dft_tools.sumk_dft import *
from triqs_dft_tools.converters.wien2k_converter import *
dft_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 DFT input
prec_mu = 0.0001
h_field = 0.0
# Solver parameters
p = {}
p["max_time"] = -1
p["length_cycle"] = 50
p["n_warmup_cycles"] = 50
p["n_cycles"] = 5000
Converter = Wien2kConverter(filename=dft_filename, repacking=True)
Converter.convert_dft_input()
mpi.barrier()
previous_runs = 0
previous_present = False
if mpi.is_master_node():
f = HDFArchive(dft_filename+'.h5','a')
if 'dmft_output' in f:
ar = f['dmft_output']
if 'iterations' in ar:
previous_present = True
previous_runs = ar['iterations']
else:
f.create_group('dmft_output')
del f
previous_runs = mpi.bcast(previous_runs)
previous_present = mpi.bcast(previous_present)
SK=SumkDFT(hdf_file=dft_filename+'.h5',use_dft_blocks=use_blocks,h_field=h_field)
n_orb = SK.corr_shells[0]['dim']
l = SK.corr_shells[0]['l']
spin_names = ["up","down"]
orb_names = [i for i in range(n_orb)]
# Use GF structure determined by DFT blocks
gf_struct = [(block, indices) for block, indices in SK.gf_struct_solver[0].iteritems()]
# 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_int = h_int_density(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U=Umat, Uprime=Upmat, H_dump="H.txt")
S = Solver(beta=beta, gf_struct=gf_struct)
if previous_present:
chemical_potential = 0
dc_imp = 0
dc_energ = 0
if mpi.is_master_node():
S.Sigma_iw << HDFArchive(dft_filename+'.h5','a')['dmft_output']['Sigma_iw']
chemical_potential,dc_imp,dc_energ = SK.load(['chemical_potential','dc_imp','dc_energ'])
S.Sigma_iw << mpi.bcast(S.Sigma_iw)
chemical_potential = mpi.bcast(chemical_potential)
dc_imp = mpi.bcast(dc_imp)
dc_energ = mpi.bcast(dc_energ)
SK.set_mu(chemical_potential)
SK.set_dc(dc_imp,dc_energ)
for iteration_number in range(1,loops+1):
if mpi.is_master_node(): print "Iteration = ", iteration_number
SK.symm_deg_gf(S.Sigma_iw,orb=0) # symmetrise Sigma
SK.set_Sigma([ S.Sigma_iw ]) # set Sigma into the SumK class
chemical_potential = SK.calc_mu( precision = prec_mu ) # find the chemical potential for given density
S.G_iw << SK.extract_G_loc()[0] # calc the local Green function
mpi.report("Total charge of Gloc : %.6f"%S.G_iw.total_density())
# Init the DC term and the real part of Sigma, if no previous runs found:
if (iteration_number==1 and previous_present==False):
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.dc_imp[0]['up'][0,0]
# 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(dft_filename+'.h5','a')['dmft_output']
if (iteration_number>1 or previous_present):
mpi.report("Mixing input Delta with factor %s"%delta_mix)
Delta = (delta_mix * delta(S.G0_iw)) + (1.0-delta_mix) * ar['Delta_iw']
S.G0_iw << S.G0_iw + delta(S.G0_iw) - Delta
ar['Delta_iw'] = delta(S.G0_iw)
S.G0_iw << inverse(S.G0_iw)
del ar
S.G0_iw << mpi.bcast(S.G0_iw)
# Solve the impurity problem:
S.solve(h_int=h_int, **p)
# Solved. Now do 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(dft_filename+'.h5','a')['dmft_output']
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(dft_filename+'.h5','a')['dmft_output']
if previous_runs: iteration_number += previous_runs
ar['iterations'] = iteration_number
ar['G_tau'] = S.G_tau
ar['G_iw'] = S.G_iw
ar['Sigma_iw'] = S.Sigma_iw
ar['G0-%s'%(iteration_number)] = S.G0_iw
ar['G-%s'%(iteration_number)] = S.G_iw
ar['Sigma-%s'%(iteration_number)] = S.Sigma_iw
del ar
# 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 dft_output group of hdf5 archive in case of rerun:
SK.save(['chemical_potential','dc_imp','dc_energ'])
if mpi.is_master_node():
ar = HDFArchive("dftdmft.h5",'w')
ar["G_tau"] = S.G_tau
ar["G_iw"] = S.G_iw
ar["Sigma_iw"] = S.Sigma_iw