mirror of
https://github.com/triqs/dft_tools
synced 2024-11-07 06:33:48 +01:00
1c29776727
* Committing missing files of last commit
* Correcting typos
* Modifications according to issue #56
152 lines
5.8 KiB
Python
152 lines
5.8 KiB
Python
import pytriqs.utility.mpi as mpi
|
|
from pytriqs.operators.util import *
|
|
from pytriqs.archive import HDFArchive
|
|
from pytriqs.applications.impurity_solvers.cthyb import *
|
|
from pytriqs.gf.local import *
|
|
from pytriqs.applications.dft.sumk_dft import *
|
|
from pytriqs.applications.dft.converters.wien2k_converter import *
|
|
|
|
dft_filename='SrVO3'
|
|
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["random_seed"] = 123 * mpi.rank + 567
|
|
p["length_cycle"] = 200
|
|
p["n_warmup_cycles"] = 100000
|
|
p["n_cycles"] = 1000000
|
|
p["perfrom_tail_fit"] = True
|
|
p["fit_max_moments"] = 4
|
|
p["fit_min_n"] = 30
|
|
p["fit_max_n"] = 60
|
|
|
|
# If conversion step was not done, we could do it here. Uncomment the lines it you want to do this.
|
|
#from pytriqs.applications.dft.converters.wien2k_converter import *
|
|
#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 = SK.gf_struct_solver[0]
|
|
|
|
# Construct Slater U matrix
|
|
Umat = U_matrix(n_orb=n_orb, U_int=U, J_hund=J, basis='cubic',)
|
|
|
|
# Construct Hamiltonian and solver
|
|
h_int = h_int_slater(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U_matrix=Umat)
|
|
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():
|
|
ar = HDFArchive(dft_filename+'.h5','a')
|
|
S.Sigma_iw << ar['dmft_output']['Sigma_iw']
|
|
del ar
|
|
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')
|
|
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['dmft_output']['Delta_iw']
|
|
S.G0_iw << S.G0_iw + delta(S.G0_iw) - Delta
|
|
ar['dmft_output']['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')
|
|
mpi.report("Mixing Sigma and G with factor %s"%sigma_mix)
|
|
S.Sigma_iw << sigma_mix * S.Sigma_iw + (1.0-sigma_mix) * ar['dmft_output']['Sigma_iw']
|
|
S.G_iw << sigma_mix * S.G_iw + (1.0-sigma_mix) * ar['dmft_output']['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')
|
|
ar['dmft_output']['iterations'] = iteration_number + previous_runs
|
|
ar['dmft_output']['G_tau'] = S.G_tau
|
|
ar['dmft_output']['G_iw'] = S.G_iw
|
|
ar['dmft_output']['Sigma_iw'] = S.Sigma_iw
|
|
ar['dmft_output']['G0-%s'%(iteration_number)] = S.G0_iw
|
|
ar['dmft_output']['G-%s'%(iteration_number)] = S.G_iw
|
|
ar['dmft_output']['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'])
|