mirror of https://github.com/triqs/dft_tools
[doc] Corrected errors in the Ce example scripts
This commit is contained in:
Manuel Zingl
parent
8652660115
commit
919e0a3ead
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@ -145,7 +145,7 @@ Most of these parameters are self-explanatory. The first,
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details on the solver parameters, we refer the user to
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the :ref:`CTHYB solver <triqscthyb:welcome>` documentation.
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We assume that the conversion to the hdf5 archive is alreadz done. We
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We assume that the conversion to the hdf5 archive is already done. We
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can check now in this archive, if previous runs are present, or if we have to start
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from scratch::
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@ -2,7 +2,6 @@ from pytriqs.applications.dft.sumk_dft import *
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from pytriqs.applications.dft.converters.wien2k_converter import *
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from pytriqs.applications.impurity_solvers.hubbard_I.hubbard_solver import Solver
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import os
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dft_filename = os.getcwd().rpartition('/')[2]
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@ -10,14 +9,12 @@ beta = 40
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U_int = 6.00
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J_hund = 0.70
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Loops = 5 # Number of DMFT sc-loops
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Mix = 0.7 # Mixing factor in QMC
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mixing = 0.7 # Mixing factor
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DC_type = 0 # 0...FLL, 1...Held, 2... AMF, 3...Lichtenstein
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chemical_potential_init=0.0 # initial chemical potential
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HDFfilename = dft_filename+'.h5'
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# Convert DMFT input:
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Converter = Wien2kConverter(filename=filename)
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Converter = Wien2kConverter(filename=dft_filename)
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Converter.convert_dft_input()
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mpi.barrier()
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@ -25,7 +22,7 @@ mpi.barrier()
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previous_runs = 0
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previous_present = False
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if mpi.is_master_node():
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f = HDFArchive(filename+'.h5','a')
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f = HDFArchive(dft_filename+'.h5','a')
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if 'dmft_output' in f:
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ar = f['dmft_output']
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if 'iterations' in ar:
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@ -53,11 +50,11 @@ if previous_present:
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ar = HDFArchive(dft_filename+'.h5','a')
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S.Sigma << ar['dmft_output']['Sigma']
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del ar
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chemical_potential,dc_imp,dc_energ = SK.load(['chemical_potential','dc_imp','dc_energ'])
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SK.chemical_potential,SK.dc_imp,SK.dc_energ = SK.load(['chemical_potential','dc_imp','dc_energ'])
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S.Sigma << mpi.bcast(S.Sigma)
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SK.set_mu(chemical_potential)
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SK.set_dc(dc_imp,dc_energ)
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SK.chemical_potential = mpi.bcast(SK.chemical_potential)
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SK.dc_imp = mpi.bcast(SK.dc_imp)
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SK.dc_energ = mpi.bcast(SK.dc_energ)
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# DMFT loop:
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for iteration_number in range(1,Loops+1):
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@ -78,7 +75,7 @@ for iteration_number in range(1,Loops+1):
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if ((iteration_number==1)and(previous_present==False)):
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dc_value_init=U_int/2.0
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dm=S.G.density()
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SK.calc_dc( dm, U_interact = U_int, J_hund = J_hund, orb = 0, use_dc_formula = DC_type, use_dc_value=dc_value_init)
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SK.calc_dc( dm, U_interact = U_int, J_hund = J_hund, orb = 0, use_dc_formula = DC_type, use_dc_value=dc_value_init)
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# calculate non-interacting atomic level positions:
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eal = SK.eff_atomic_levels()[0]
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@ -89,11 +86,11 @@ for iteration_number in range(1,Loops+1):
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# Now mix Sigma and G with factor Mix, if wanted:
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if (iteration_number>1 or previous_present):
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if (mpi.is_master_node() and (sigma_mix<1.0)):
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if (mpi.is_master_node() and (mixing<1.0)):
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ar = HDFArchive(dft_filename+'.h5','a')
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mpi.report("Mixing Sigma and G with factor %s"%sigma_mix)
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S.Sigma << sigma_mix * S.Sigma + (1.0-sigma_mix) * ar['dmft_output']['Sigma']
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S.G << sigma_mix * S.G + (1.0-sigma_mix) * ar['dmft_output']['G']
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mpi.report("Mixing Sigma and G with factor %s"%mixing)
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S.Sigma << mixing * S.Sigma + (1.0-mixing) * ar['dmft_output']['Sigma']
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S.G << mixing * S.G + (1.0-mixing) * ar['dmft_output']['G']
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del ar
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S.G << mpi.bcast(S.G)
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S.Sigma << mpi.bcast(S.Sigma)
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@ -104,8 +101,8 @@ for iteration_number in range(1,Loops+1):
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SK.calc_dc( dm, U_interact = U_int, J_hund = J_hund, orb = 0, use_dc_formula = DC_type )
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# correlation energy calculations:
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correnerg = 0.5 * (S.G * S.Sigma).total_density()
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mpi.report("Corr. energy = %s"%correnerg)
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SK.correnerg = 0.5 * (S.G * S.Sigma).total_density()
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mpi.report("Corr. energy = %s"%SK.correnerg)
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# store the impurity self-energy, GF as well as correlation energy in h5
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if mpi.is_master_node():
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@ -145,7 +142,7 @@ mpi.report("Trace of Density Matrix: %s"%d)
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# store correlation energy contribution to be read by Wien2ki and then included to DFT+DMFT total energy
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if (mpi.is_master_node()):
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correnerg -= DCenerg[0]
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SK.correnerg -= SK.dc_energ[0]
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f=open(dft_filename+'.qdmft','a')
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f.write("%.16f\n"%correnerg)
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f.write("%.16f\n"%SK.correnerg)
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f.close()
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@ -14,24 +14,22 @@ ommax=6.0
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N_om=2001
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broadening = 0.02
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HDFfilename = dft_filename+'.h5'
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# Convert DMFT input:
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Converter = Wien2kConverter(filename=dft_filename,repacking=True)
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Converter.convert_dft_input()
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Converter.convert_parproj_input()
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# Init the SumK class
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SK = SumkDFTTools(hdf_file=dft_filename+'.h5',use_dft_blocks=False)
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# load old chemical potential and DC
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if mpi.is_master_node():
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chemical_potential,dc_imp,dc_energ = SK.load(['chemical_potential','dc_imp','dc_energ'])
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SK.set_mu(chemical_potential)
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SK.set_dc(dc_imp,dc_energ)
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SK.chemical_potential,SK.dc_imp,SK.dc_energ = SK.load(['chemical_potential','dc_imp','dc_energ'])
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SK.chemical_potential = mpi.bcast(SK.chemical_potential)
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SK.dc_imp = mpi.bcast(SK.dc_imp)
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SK.dc_energ = mpi.bcast(SK.dc_energ)
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if (mpi.is_master_node()):
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print 'DC after reading SK: ',SK.dc_imp[0]
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