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advanced example in docs corrected

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Leonid Pourovskii 2014-05-30 02:07:07 +02:00
parent c11a85ffd6
commit 90e2379195
1 changed files with 39 additions and 42 deletions
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doc/advanced.rst
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@ -16,20 +16,21 @@ First, we load the necessary modules::
Then we define some parameters:: Then we define some parameters::
lda_filename='srvo3' lda_filename='srvo3'
U = 4.0 U = 2.7
J = 0.65 J = 0.65
beta = 40 beta = 40
loops = 10 # Number of DMFT sc-loops loops = 1 # Number of DMFT sc-loops
mix = 1.0 # Mixing factor of Sigma after solution of the AIM mix = 0.8 # Mixing factor of Sigma after solution of the AIM
Delta_mix = 1.0 # Mixing factor of Delta as input for the AIM Delta_mix = 1.0 # Mixing factor of Delta as input for the AIM
dc_type = 1 # DC type: 0 FLL, 1 Held, 2 AMF dc_type = 1 # DC type: 0 FLL, 1 Held, 2 AMF
use_blocks = True # use bloc structure from LDA input use_blocks = True # use bloc structure from LDA input
use_matrix = False # True: Slater parameters, False: Kanamori parameters U, U-2J, U-3J use_matrix = False # True: Slater parameters, False: Kanamori parameters U+2J, U, U-J
use_spinflip = False # use the full rotational invariant interaction? use_spinflip = False # use the full rotational invariant interaction?
prec_mu = 0.0001 prec_mu = 0.0001
qmc_cycles = 20000 qmc_cycles = 200000
length_cycle = 200 length_cycle = 200
warming_iterations = 2000 warming_iterations = 10000
Most of these parameters are self-explaining. The first, `lda_filename`, gives the filename of the input files. Most of these parameters are self-explaining. The first, `lda_filename`, gives the filename of the input files.
The next step, as described in the previous section, is to convert the input files:: The next step, as described in the previous section, is to convert the input files::
@ -98,37 +99,33 @@ previous section, with some additional refinement::
SK.set_dc( dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type) SK.set_dc( dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type)
S.Sigma <<= SK.dc_imp[0]['up'][0,0] S.Sigma <<= SK.dc_imp[0]['up'][0,0]
# now calculate new G0: S.G0 <<= inverse(S.Sigma + inverse(S.G))
if (mpi.is_master_node()):
# We can do a mixing of Delta in order to stabilize the DMFT iterations:
S.G0 <<= S.Sigma + inverse(S.G)
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.delta()) + (1.0-Delta_mix) * ar['DeltaF']
S.G0 <<= S.G0 + S.G0.delta() - Delta
ar['DeltaF'] = S.G0.delta()
S.G0 <<= inverse(S.G0)
del ar
S.G0 = mpi.bcast(S.G0)
# Solve the impurity problem: # Solve the impurity problem:
S.Solve(U_interact=U,J_hund=J,n_orb=Norb,use_matrix=use_matrix, S.solve(U_interact=U,J_hund=J,use_spinflip=use_spinflip,use_matrix=use_matrix,
T=SK.T[0], gf_struct=SK.gf_struct_solver[0],map=SK.map[0], l=l,T=SK.T[0], dim_reps=SK.dim_reps[0], irep=2, deg_orbs=SK.deg_shells[0],n_cycles =qmc_cycles,
l=l, deg_orbs=SK.deg_shells[0], use_spinflip=use_spinflip, length_cycle=length_cycle,n_warmup_cycles=warming_iterations)
n_cycles =qmc_cycles,length_cycle=length_cycle,n_warmup_cycles=warming_iterations)
# solution done, do the post-processing: # solution done, do the post-processing:
mpi.report("Total charge of impurity problem : %.6f"%S.G.total_density()) mpi.report("Total charge of impurity problem : %.6f"%S.G.total_density())
S.Sigma <<=(inverse(S.G0)-inverse(S.G))
# Solve the impurity problem:
S.solve(U_interact=U,J_hund=J,use_spinflip=use_spinflip,use_matrix=use_matrix,
l=l,T=SK.T[0], dim_reps=SK.dim_reps[0], irep=2, deg_orbs=SK.deg_shells[0],n_cycles =qmc_cycles,
length_cycle=length_cycle,n_warmup_cycles=warming_iterations)
# solution done, do the post-processing:
mpi.report("Total charge of impurity problem : %.6f"%S.G.total_density())
S.Sigma <<=(inverse(S.G0)-inverse(S.G))
# Now mix Sigma and G with factor Mix, if wanted: # Now mix Sigma and G with factor Mix, if wanted:
if ((iteratio_number>1) or (previous_present)): if ((iteration_number>1) or (previous_present)):
if (mpi.is_master_node()): if (mpi.is_master_node()):
ar = HDFArchive(lda_filename+'.h5','a') ar = HDFArchive(lda_filename+'.h5','a')
mpi.report("Mixing Sigma and G with factor %s"%mix) mpi.report("Mixing Sigma and G with factor %s"%mix)
S.Sigma <<= mix * S.Sigma + (1.0-mix) * ar['SigmaF'] S.Sigma <<= mix * S.Sigma + (1.0-mix) * ar['Sigma']
S.G <<= mix * S.G + (1.0-mix) * ar['GF'] S.G <<= mix * S.G + (1.0-mix) * ar['GF']
del ar del ar
S.G = mpi.bcast(S.G) S.G = mpi.bcast(S.G)
@ -138,15 +135,15 @@ previous section, with some additional refinement::
if (mpi.is_master_node()): if (mpi.is_master_node()):
ar = HDFArchive(lda_filename+'.h5','a') ar = HDFArchive(lda_filename+'.h5','a')
ar['iterations'] = previous_runs + iteration_number ar['iterations'] = previous_runs + iteration_number
ar['SigmaF'] = S.Sigma ar['Sigma'] = S.Sigma
ar['GF'] = S.G ar['GF'] = S.G
del ar del ar
save_Gf(S.Sigma,'Sigma')
# Now set new double counting: # Now set new double counting:
dm = S.G.density() dm = S.G.density()
SK.set_dc( dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type) SK.set_dc( dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type)
#Save stuff: #Save stuff:
SK.save() SK.save()
This is all we need for the LDA+DMFT calculation. At the end, all results are stored in the hdf5 output file. This is all we need for the LDA+DMFT calculation. At the end, all results are stored in the hdf5 output file.