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More changes to template file.

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Priyanka Seth 2015-01-15 19:43:38 +01:00
parent 54827163ce
commit 59837c68b2
1 changed files with 68 additions and 64 deletions
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70
lda_dmft_cthyb.py
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@ -16,12 +16,11 @@ delta_mix = 1.0 # Mixing factor of Delta as input for the AIM
dc_type = 0 # DC type: 0 FLL, 1 Held, 2 AMF dc_type = 0 # DC type: 0 FLL, 1 Held, 2 AMF
use_blocks = True # use bloc structure from DFT input use_blocks = True # use bloc structure from DFT input
prec_mu = 0.0001 prec_mu = 0.0001
h_field = 0.0
# Solver parameters # Solver parameters
p = {} p = {}
p["max_time"] = -1 p["max_time"] = -1
p["random_name"] = ""
p["random_seed"] = 123 * mpi.rank + 567
p["length_cycle"] = 50 p["length_cycle"] = 50
p["n_warmup_cycles"] = 50 p["n_warmup_cycles"] = 50
p["n_cycles"] = 5000 p["n_cycles"] = 5000
@ -44,88 +43,92 @@ if mpi.is_master_node():
del f del f
previous_runs = mpi.bcast(previous_runs) previous_runs = mpi.bcast(previous_runs)
previous_present = mpi.bcast(previous_present) 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=SumkDFT(hdf_file=dft_filename+'.h5',use_dft_blocks=calc_blocs) SK=SumkDFT(hdf_file=dft_filename+'.h5',use_dft_blocks=use_blocks,h_field=h_field)
n_orb = SK.corr_shells[0]['dim'] n_orb = SK.corr_shells[0]['dim']
l = SK.corr_shells[0]['l'] l = SK.corr_shells[0]['l']
spin_names = ["up","down"] spin_names = ["up","down"]
orb_names = ["%s"%i for i in range(num_orbitals)] orb_names = [i for i in range(n_orb)]
orb_hybridized = False
# Construct U matrix for density-density calculations # Use GF structure determined by DFT blocks
gf_struct = set_operator_structure(spin_names,orb_names,orb_hybridized) gf_struct = SK.gf_struct_solver[0]
# Construct U matrix for density-density calculations # Construct U matrix for density-density calculations
Umat, Upmat = U_matrix_kanamori(n_orb=n_orb, U_int=U, J_hund=J) Umat, Upmat = U_matrix_kanamori(n_orb=n_orb, U_int=U, J_hund=J)
# Construct Hamiltonian and solver # Construct Hamiltonian and solver
H = h_loc_density(spin_names, orb_names, orb_hybridized, U=Umat, Uprime=Upmat, H_dump="H.txt") h_loc = h_loc_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) S = Solver(beta=beta, gf_struct=gf_struct)
if (previous_present): if previous_present:
if (mpi.is_master_node()): if mpi.is_master_node():
S.Sigma_iw << HDFArchive(dft_filename+'.h5','a')['dmft_output']['Sigma_iw'] S.Sigma_iw << HDFArchive(dft_filename+'.h5','a')['dmft_output']['Sigma_iw']
S.Sigma_iw = mpi.bcast(S.Sigma_iw) chemical_potential,dc_imp,dc_energ = SK.load(['chemical_potential','dc_imp','dc_energ'])
S.Sigma_iw << mpi.bcast(S.Sigma_iw)
SK.set_mu(chemical_potential)
SK.set_dc(dc_imp,dc_energ)
for iteration_number in range(1,loops+1): for iteration_number in range(1,loops+1):
if mpi.is_master_node(): print "Iteration = ", i if mpi.is_master_node(): print "Iteration = ", iteration_number
SK.symm_deg_gf(S.Sigma_iw,orb=0) # symmetrise Sigma 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 SK.put_Sigma(Sigma_imp = [ S.Sigma_iw ]) # put Sigma into the SumK class
chemical_potential = SK.calc_mu( precision = prec_mu ) # find the chemical potential for the given density chemical_potential = SK.calc_mu( precision = prec_mu ) # find the chemical potential for given density
S.G_iw << SK.extract_G_loc()[0] # extract the local Green function 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()) 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 runs found:
# Init the DC term and the real part of Sigma, if no previous run was found: if (iteration_number==1 and previous_present==False):
dm = S.G_iw.density() dm = S.G_iw.density()
SK.calc_dc(dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type) 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] S.Sigma_iw << SK.dc_imp[0]['up'][0,0]
# now calculate new G0_iw to input into the solver: # Calculate new G0_iw to input into the solver:
if (mpi.is_master_node()): if mpi.is_master_node():
# We can do a mixing of Delta in order to stabilize the DMFT iterations: # We can do a mixing of Delta in order to stabilize the DMFT iterations:
S.G0_iw << S.Sigma_iw + inverse(S.G_iw) S.G0_iw << S.Sigma_iw + inverse(S.G_iw)
ar = HDFArchive(dft_filename+'.h5','a')['dmft_output'] ar = HDFArchive(dft_filename+'.h5','a')['dmft_output']
if ((iteration_number>1) or (previous_present)): if (iteration_number>1 or previous_present):
mpi.report("Mixing input Delta with factor %s"%delta_mix) mpi.report("Mixing input Delta with factor %s"%delta_mix)
Delta = (delta_mix * delta(S.G0_iw)) + (1.0-delta_mix) * ar['Delta_iw'] 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 S.G0_iw << S.G0_iw + delta(S.G0_iw) - Delta
ar['Delta_iw'] = delta(S.G0_iw) ar['Delta_iw'] = delta(S.G0_iw)
S.G0_iw << inverse(S.G0_iw) S.G0_iw << inverse(S.G0_iw)
del ar del ar
S.G0_iw = mpi.bcast(S.G0_iw) S.G0_iw << mpi.bcast(S.G0_iw)
# Solve the impurity problem: # Solve the impurity problem:
S.solve(h_loc=h_loc, **p) S.solve(h_loc=h_loc, **p)
# solution done, do the post-processing: # Solved. Now do post-processing:
mpi.report("Total charge of impurity problem : %.6f"%S.G_iw.total_density()) mpi.report("Total charge of impurity problem : %.6f"%S.G_iw.total_density())
# Now mix Sigma and G with factor sigma_mix, if wanted: # Now mix Sigma and G with factor sigma_mix, if wanted:
if ((iteration_number>1) or (previous_present)): if (iteration_number>1 or previous_present):
if (mpi.is_master_node()): if mpi.is_master_node():
ar = HDFArchive(dft_filename+'.h5','a')['dmft_output'] ar = HDFArchive(dft_filename+'.h5','a')['dmft_output']
mpi.report("Mixing Sigma and G with factor %s"%sigma_mix) 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.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'] S.G_iw << sigma_mix * S.G_iw + (1.0-sigma_mix) * ar['G_iw']
del ar del ar
S.G_iw = mpi.bcast(S.G_iw) S.G_iw << mpi.bcast(S.G_iw)
S.Sigma_iw = mpi.bcast(S.Sigma_iw) S.Sigma_iw << mpi.bcast(S.Sigma_iw)
# Write the final Sigma and G to the hdf5 archive: # Write the final Sigma and G to the hdf5 archive:
if (mpi.is_master_node()): if mpi.is_master_node():
ar = HDFArchive(dft_filename+'.h5','a') ar = HDFArchive(dft_filename+'.h5','a')['dmft_output']
ar['iterations'] = previous_runs + iteration_number if previous_runs: iteration_number += previous_runs
ar['Sigma_iw'] = S.Sigma_iw ar['iterations'] = iteration_number
ar['G_tau'] = S.G_tau
ar['G_iw'] = S.G_iw 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 del ar
# Set the new double counting:
dm = S.G_iw.density() # compute the density matrix of the impurity problem dm = S.G_iw.density() # compute the density matrix of the impurity problem
# Set the double counting
SK.calc_dc(dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type) 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: # Save stuff into the dft_output group of hdf5 archive in case of rerun:
@ -133,5 +136,6 @@ for iteration_number in range(1,loops+1):
if mpi.is_master_node(): if mpi.is_master_node():
ar = HDFArchive("dftdmft.h5",'w') ar = HDFArchive("dftdmft.h5",'w')
ar["G_tau"] = S.G_tau
ar["G_iw"] = S.G_iw ar["G_iw"] = S.G_iw
ar["Sigma_iw"] = S.Sigma_iw ar["Sigma_iw"] = S.Sigma_iw