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https://github.com/triqs/dft_tools
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Documentation and tutorial update
This commit is contained in:
Leonid Pourovskii
parent
6ac850521f
commit
27dd24c490
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@ -178,7 +178,7 @@ Then one needs to load projectors needed for calculations of corresponding proje
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Then after the solver initialization and setting up atomic levels we compute atomic Green's function and self-energy on the real axis::
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S.set_atomic_levels( eal = eal )
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S.GF_realomega(ommin=ommin, ommax = ommax, N_om=N_om)
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S.GF_realomega(ommin=ommin, ommax = ommax, N_om=N_om,U_int=U_int,J_hund=J_hund)
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put it into SK class and then calculated the actual DOS::
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@ -2,22 +2,25 @@ from pytriqs.applications.dft.sumk_lda 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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LDAFilename = 'Ce'
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Beta = 40
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Uint = 6.00
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JHund = 0.70
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lda_filename = 'Ce-gamma'
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beta = 40
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U_int = 6.00
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J_hund = 0.70
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Loops = 2 # Number of DMFT sc-loops
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Mix = 0.7 # Mixing factor in QMC
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DC_type = 0 # 0...FLL, 1...Held, 2... AMF, 3...Lichtenstein
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DC_Mix = 1.0 # 1.0 ... all from imp; 0.0 ... all from Gloc
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useBlocs = False # use bloc structure from LDA input
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useMatrix = True # use the U matrix calculated from Slater coefficients instead of (U+2J, U, U-J)
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Natomic = 1
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HDFfilename = LDAFilename+'.h5'
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HDFfilename = lda_filename+'.h5'
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use_val= U_int * (Natomic - 0.5) - J_hund * (Natomic*0.5 - 0.5)
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# Convert DMFT input:
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# Can be commented after the first run
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Converter = Wien2kConverter(filename=LDAFilename)
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Converter = Wien2kConverter(filename=lda_filename)
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Converter.convert_dmft_input()
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#check if there are previous runs:
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@ -39,13 +42,13 @@ previous_runs = mpi.bcast(previous_runs)
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previous_present = mpi.bcast(previous_present)
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# Init the SumK class
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SK=SumkLDA(hdf_file=LDAFilename+'.h5',use_lda_blocks=False)
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SK=SumkLDA(hdf_file=lda_filename+'.h5',use_lda_blocks=False)
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Norb = SK.corr_shells[0][3]
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l = SK.corr_shells[0][2]
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# Init the Solver:
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S = Solver(beta = Beta, l = l)
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S = Solver(beta = beta, l = l)
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if (previous_present):
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# load previous data:
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@ -77,7 +80,7 @@ for Iteration_Number in range(1,Loops+1):
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dm = S.G.density()
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if ((Iteration_Number==1)and(previous_present==False)):
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SK.set_dc( dens_mat=dm, U_interact = Uint, J_hund = JHund, orb = 0, use_dc_formula = DC_type)
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SK.set_dc( dens_mat=dm, U_interact = U_int, J_hund = J_hund, orb = 0, use_dc_formula = DC_type, use_val=use_val)
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# set atomic levels:
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eal = SK.eff_atomic_levels()[0]
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@ -90,7 +93,7 @@ for Iteration_Number in range(1,Loops+1):
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del ar
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# solve it:
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S.solve(U_int = Uint, J_hund = JHund, verbosity = 1)
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S.solve(U_int = U_int, J_hund = J_hund, verbosity = 1)
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if (mpi.is_master_node()):
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ar = HDFArchive(HDFfilename)
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@ -116,7 +119,7 @@ for Iteration_Number in range(1,Loops+1):
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# after the Solver has finished, set new double counting:
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dm = S.G.density()
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SK.set_dc( dm, U_interact = Uint, J_hund = JHund, orb = 0, use_dc_formula = DC_type )
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SK.set_dc( dm, U_interact = U_int, J_hund = J_hund, orb = 0, use_dc_formula = DC_type , use_val=use_val)
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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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@ -151,7 +154,7 @@ for Iteration_Number in range(1,Loops+1):
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# find exact chemical potential
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if (SK.density_required):
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SK.chemical_potential = SK.find_mu( precision = 0.000001 )
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dN,d = SK.calc_density_correction(filename = LDAFilename+'.qdmft')
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dN,d = SK.calc_density_correction(filename = lda_filename+'.qdmft')
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mpi.report("Trace of Density Matrix: %s"%d)
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@ -163,7 +166,6 @@ if (mpi.is_master_node()):
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DCenerg = ar['DCenerg%s'%itn]
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del ar
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correnerg -= DCenerg[0]
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f=open(LDAFilename+'.qdmft','a')
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f=open(lda_filename+'.qdmft','a')
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f.write("%.16f\n"%correnerg)
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f.close()
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@ -1,13 +1,13 @@
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from pytriqs.applications.dft.sumk_lda_tools import *
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from pytriqs.applications.dft.converters.wien2k_converter import *
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from pytriqs.applications.impurity_solvers.hubbard_I.solver import Solver
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from pytriqs.applications.impurity_solvers.hubbard_I.hubbard_solver import Solver
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# Creates the data directory, cd into it:
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#Prepare_Run_Directory(DirectoryName = "Ce-Gamma")
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LDAFilename = 'Ce-gamma'
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lda_filename = 'Ce-gamma'
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Beta = 40
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Uint = 6.00
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JHund = 0.70
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U_int = 6.00
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J_hund = 0.70
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DC_type = 0 # 0...FLL, 1...Held, 2... AMF, 3...Lichtenstein
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load_previous = True # load previous results
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useBlocs = False # use bloc structure from LDA input
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@ -17,13 +17,13 @@ ommax=6.0
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N_om=2001
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broadening = 0.02
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HDFfilename = LDAFilename+'.h5'
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HDFfilename = lda_filename+'.h5'
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# Convert DMFT input:
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# Can be commented after the first run
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Converter = Wien2kConverter(filename=LDAFilename,repacking=True)
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Converter = Wien2kConverter(filename=lda_filename,repacking=True)
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Converter.convert_dmft_input()
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Converter.convert_par_proj_input()
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Converter.convert_parproj_input()
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#check if there are previous runs:
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previous_runs = 0
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@ -48,7 +48,7 @@ previous_present = mpi.bcast(previous_present)
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# from a converted h5 archive.
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# Init the SumK class
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SK = SumkLDATools(hdf_file=LDAFilename+'.h5',use_lda_blocks=False)
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SK = SumkLDATools(hdf_file=lda_filename+'.h5',use_lda_blocks=False)
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if (mpi.is_master_node()):
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@ -58,13 +58,12 @@ N = SK.corr_shells[0][3]
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l = SK.corr_shells[0][2]
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# Init the Solver:
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S = Solver(Beta = Beta, Uint = Uint, JHund = JHund, l = l)
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S.Nmoments=10
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S = Solver(beta = Beta, l = l)
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S.Nmoments= 8
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# set atomic levels:
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eal = SK.eff_atomic_levels()[0]
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S.set_atomic_levels( eal = eal )
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S.GF_realomega(ommin=ommin, ommax = ommax, N_om=N_om)
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S.Sigma.save('S.Sigma')
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SK.put_Sigma(Sigmaimp = [S.Sigma])
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S.GF_realomega(ommin=ommin, ommax = ommax, N_om=N_om,U_int=U_int,J_hund=J_hund)
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SK.put_Sigma(Sigma_imp = [S.Sigma])
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SK.dos_partial(broadening=broadening)
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@ -53,10 +53,12 @@ specific needs. They are:
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The solver method is called later by this statement::
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S.solve(U_interact = U, J_hund = J)
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S.solve(U_interact,J_hund,use_spinflip=False,use_matrix=True,
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l=2,T=None, dim_reps=None, irep=None, deg_orbs=[],n_cycles =10000,
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length_cycle=200,n_warmup_cycles=1000)
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The parameters for the Coulomb interaction `U_interact` and the Hunds coupling `J_hund` are necessary parameters.
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The following parameters are optional, by highly recommended to be set:
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The parameters for the Coulomb interaction `U_interact` and the Hunds coupling `J_hund` are necessary parameters. The rest are optional parameters, for which default values are set.
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They denerally should be reset for a given problem. Their meaning is as follows:
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* `use_matrix`: If `True`, the interaction matrix is calculated from Slater integrals, which are calculated from `U_interact` and
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`J_hund`. Otherwise, a Kanamori representation is used. Attention: We define the intraorbital interaction as
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@ -65,22 +67,20 @@ The following parameters are optional, by highly recommended to be set:
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* `T`: A matrix that transforms the interaction matrix from spherical harmonics, to a symmetry adapted basis. Only effective, if
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`use_matrix=True`.
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* `l`: Orbital quantum number. Again, only effective for Slater parametrisation.
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* `deg_shells`: A list that gives the degeneracies of the orbitals. It is used to set up a global move of the CTQMC solver.
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* `deg_orbs`: A list that gives the degeneracies of the orbitals. It is used to set up a global move of the CTQMC solver.
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* `use_spinflip`: If `True`, the full rotationally-invariant interaction is used. Otherwise, only density-density terms are
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kept in the local Hamiltonian.
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* `dim_reps`: If only a subset of the full d-shell is used a correlated orbtials, one can specify here the dimensions of all the subspaces
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of the d-shell, i.e. t2g and eg. Only effective for Slater parametrisation.
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* `irep`: The index in the list `dim_reps` of the subset that is used. Only effective for Slater parametrisation.
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* `n_cycles`: Number of CTQMC cycles (a sequence of moves followed by a measurement) per core. The default value of 10000 is the minimum, and generally should be incresed
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* `length_cycle`: Number of CTQMC moves per one cycle
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* `n_warmup_cycles`: Number of initial CTQMC cycles before measurements start. Usually of order of 10000, sometimes needs to be increased significantly.
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Most of above parameters can be taken directly from the :class:`SumkLDA` class, without defining them by hand. We will see a specific example
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at the end of this tutorial.
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After initialisation, several other CTQMC parameters can be set (see CTQMC doc). The most important are:
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* `S.n_cycles`: Number of QMC cycles per node.
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* `S.n_warmup_cycles`: Number of iterations used for thermalisation
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After initialisation, several other CTQMC parameters can be set (see CTQMC doc).
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.. index:: LDA+DMFT loop, one-shot calculation
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@ -120,9 +120,9 @@ At the end of the calculation, we can save the Greens function and self energy i
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from pytriqs.archive import HDFArchive
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import pytriqs.utility.mpi as mpi
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if mpi.is_master_node():
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R = HDFArchive("YourLDADMFTcalculation.h5",'w')
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R["G"] = S.G
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R["Sigma"] = S.Sigma
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ar = HDFArchive("YourLDADMFTcalculation.h5",'w')
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ar["G"] = S.G
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ar["Sigma"] = S.Sigma
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This is it!
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@ -67,11 +67,7 @@ The next step is to initialise the Solver::
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S = SolverMultiBand(beta=beta,n_orb=Norb,gf_struct=SK.gf_struct_solver[0],map=SK.map[0])
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As we can see, many options of the solver are set by properties of the :class:`SumkLDA` class, so we don't have
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to set them manually. We now set the basic parameters of the QMC solver::
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S.n_cycles = qmc_cycles
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S.length_cycle = length_cycle
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S.n_warmup_cycles = warming_iterations
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to set them manually.
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If there are previous runs stored in the hdf5 archive, we can now load the self energy
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of the last iteration::
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@ -121,7 +117,8 @@ previous section, with some additional refinement::
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# Solve the impurity problem:
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S.Solve(U_interact=U,J_hund=J,n_orb=Norb,use_matrix=use_matrix,
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T=SK.T[0], gf_struct=SK.gf_struct_solver[0],map=SK.map[0],
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l=l, deg_orbs=SK.deg_shells[0], use_spinflip=use_spinflip))
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l=l, deg_orbs=SK.deg_shells[0], use_spinflip=use_spinflip,
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n_cycles =qmc_cycles,length_cycle=length_cycle,n_warmup_cycles=warming_iterations)
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# solution done, do the post-processing:
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mpi.report("Total charge of impurity problem : %.6f"%S.G.total_density())
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@ -66,19 +66,28 @@ Routines with real-frequency self energy
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----------------------------------------
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In order to plot data including correlation effects on the real axis, one has to provide the real frequency self energy.
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Most conveniently, it is stored as a real frequency :class:`BlockGf` object in the hdf5 file. There is one important thing to
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keep in mind. The real frequency self energy has to carry the note `ReFreq`::
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Most conveniently, it is stored as a real frequency :class:`BlockGf` object in the hdf5 file::
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SigmaReFreq.note = 'ReFreq'
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This tells the SumkLDA routines, that it is indeed a real frequency Greens function. Supposed you have your self energy now
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in the archive, you can type::
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ar=HDFArchive(SK.hdf_file)
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SK.put_Sigma([ ar['SigmaReFreq'] ])
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ar = HDFArchive(filename+'.h5','a')
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ar['SigmaReFreq'] = Sigma_real
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del ar
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This loads the self energy and puts it into the :class:`SumkLDA` class. The chemical potential as well as the double
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You may also store it in text files. If all blocks of your self energy are of dimension 1x1 you store them in `filename_(block)0.dat` files. Here `(block)` is a block name (`up`, `down`, or combined `ud`). In the case when you have matrix blocks, you store them in `(i)_(j).dat` files in the `filename_(block)` directory
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This self energy is loaded and put into the :class:`SumkLDA` class by the function::
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SK.constr_Sigma_real_axis(filename, hdf=True, hdf_dataset='SigmaReFreq',n_om=0)
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where:
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* `filename` is the file name of the hdf5 archive file or the `fname` pattern in text files names as described above.
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* `hdf=True` the real-axis self energy will be read from the hdf5 file, `hdf=False`: from the text files
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* `hdf_dataset` the name of dataset where the self energy is stored in the hdf5 file
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* `n_om` number of points in the real-axis mesh (used only if `hdf=False`)
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The chemical potential as well as the double
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counting correction was already read in the initialisation process.
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With this self energy, we can do now::
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