mirror of https://github.com/triqs/dft_tools
159 lines
9.3 KiB
ReStructuredText
159 lines
9.3 KiB
ReStructuredText
.. index:: DFT+DMFT calculation
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.. highlight:: python
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.. _DFTDMFTmain:
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The DFT+DMFT calculation
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========================
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After having set up the hdf5 archive, we can now do our DFT+DMFT calculation. It consists of
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initialisation steps, and the actual DMFT self consistency loop.
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.. index:: initialisation of DFT+DMFT
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Initialisation of the calculation
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---------------------------------
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Before doing the calculation, we have to intialize all the objects that we will need. The first thing is the
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:class:`SumkDFT` class. It contains all basic routines that are necessary to perform a summation in k-space
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to get the local quantities used in DMFT. It is initialized by::
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from pytriqs.applications.dft.sumk_dft import *
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SK = SumkDFT(hdf_file = filename)
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This is the reference for the function:
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.. function:: SumkDFT(hdf_file, h_field = 0.0, use_dft_blocks = False, dft_data = 'dft_input', symmcorr_data = 'dft_symmcorr_input', parproj_data = 'dft_parproj_input', symmpar_data = 'dft_symmpar_input', bands_data = 'dft_bands_input', transp_data = 'dft_transp_input', misc_data = 'dft_misc_input')
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The parameters needed to initialise SumkDFT as as follows (with any default variables as shown above):
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========================= ==================== ===========================================================================
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Name Type Meaning
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========================= ==================== ===========================================================================
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hdf_file String Name of the main hdf5 file containing converted dft information.
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h_field Scalar External magnetic field.
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use_dft_blocks Boolean Analyse the structure of the density matrix at initialisation,
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and identify non-zero matrix elements.
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The DMFT calculation is then restricted to these matrix elements,
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yielding a more efficient solution of the local interaction problem.
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Degeneracies in orbital and spin space are also identified and stored for later use.
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dft_data String hdf5 subgroup containing required DFT data.
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symmcorr_data String hdf5 subgroup containing correlated shell symmetry data.
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parproj_data String hdf5 subgroup containing partial projector data.
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symmpar_data String hdf5 subgroup containing non-correlated shell symmetry data.
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bands_data String hdf5 subgroup containing DFT band data.
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transp_data String hdf5 subgroup containing optics/transport data.
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misc_data String hdf5 subgroup containing miscellaneous DFT data.
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========================= ==================== ===========================================================================
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.. index:: Multiband solver
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Setting up the Multi-Band Solver
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--------------------------------
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The next step is to setup the impurity solver.
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For more details here, see the `CTHYB <http://ipht.cea.fr/triqs/1.2/applications/cthyb/>`_ documentation.
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.. This is initialised as follows::
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..
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.. from pytriqs.applications.impurity_solvers.cthyb import *
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.. beta = 40.0
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.. gf_struct = SK.gf_struct_solver[0]
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.. S = Solver(beta=beta, gf_struct=gf_struct)
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..
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.. The solver method is called later by this statement::
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..
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.. S.solve(h_loc=h_loc, **p)
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..
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.. where `p` represents the solve parameters.
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..
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.. There is a module that helps setting up the multiband CTQMC solver. It is loaded and initialized by::
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..
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.. from pytriqs.applications.dft.solver_multiband import *
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.. S = SolverMultiBand(beta, n_orb, gf_struct = SK.gf_struct_solver[0], map=SK.map[0])
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..
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.. The necessary parameters are the inverse temperature `beta`, the Coulomb interaction `U_interact`, the Hund's rule coupling `J_hund`,
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.. and the number of orbitals `n_orb`. There are again several optional parameters that allow the tailoring of the local Hamiltonian to
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.. specific needs. They are:
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..
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.. * `gf_struct`: The block structure of the local density matrix given in the format calculated by :class:`SumkDFT`.
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.. * `map`: If `gf_struct` is given as parameter, `map` also must be given. This is the mapping from the block structure to a general
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.. up/down structure.
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..
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.. The solver method is called later by this statement::
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..
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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, n_cycles =10000,
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.. length_cycle=200,n_warmup_cycles=1000)
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..
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.. The parameters for the Coulomb interaction `U_interact` and the Hund's coupling `J_hund` are necessary input parameters. The rest are optional
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.. parameters for which default values are set. Generally, they should be reset for the problem at hand. Here is a description of the parameters:
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..
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.. * `use_matrix`: If `True`, the interaction matrix is calculated from Slater integrals, which are computed 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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.. `U_interact`, the interorbital interaction for opposite spins as `U_interact-2*J_hund`, and interorbital for equal spins as
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.. `U_interact-3*J_hund`.
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.. * `T`: The matrix that transforms the interaction matrix from spherical harmonics to a symmetry-adapted basis. Only effective for Slater
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.. parametrisation, i.e. `use_matrix=True`.
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.. * `l`: The orbital quantum number. Again, only effective for Slater parametrisation, i.e. `use_matrix=True`.
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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 as 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 increased.
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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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..
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.. Most of above parameters can be taken directly from the :class:`SumkDFT` 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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.. index:: DFT+DMFT loop, one-shot calculation
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Doing the DMFT loop
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-------------------
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Having initialised the SumK class and the Solver, we can proceed with the DMFT loop itself. As explained in the tutorial, we have to
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set up the loop over DMFT iterations and the self-consistency condition::
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n_loops = 5
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for iteration_number in range(n_loops) : # start the DMFT loop
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SK.put_Sigma(Sigma_imp = [ S.Sigma ]) # Put self energy to the SumK class
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chemical_potential = SK.calc_mu() # calculate the chemical potential for the given density
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S.G_iw << SK.extract_G_loc()[0] # extract the local Green function
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S.G0_iw << inverse(S.Sigma_iw + inverse(S.G_iw)) # finally get G0, the input for the Solver
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S.solve(h_loc=h_loc, **p) # now solve the impurity problem
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dm = S.G_iw.density() # Density matrix of the impurity problem
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SK.calc_dc(dm, U_interact=U, J_hund=J, orb=0, use_dc_formula=dc_type) # Set the double counting term
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SK.save(['chemical_potential','dc_imp','dc_energ']) # Save data in the hdf5 archive
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These basic steps are enough to set up the basic DMFT Loop. For a detailed description of the :class:`SumkDFT` routines,
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see the reference manual. After the self-consistency steps, the solution of the Anderson impurity problem is calculation by CTQMC.
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Different to model calculations, we have to do a few more steps after this, because of the double-counting correction. We first
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calculate the density of the impurity problem. Then, the routine `calc_dc` takes as parameters this density matrix, the
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Coulomb interaction, Hund's rule coupling, and the type of double-counting that should be used. Possible values for `use_dc_formula` are:
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* `0`: Full-localised limit
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* `1`: DC formula as given in K. Held, Adv. Phys. 56, 829 (2007).
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* `2`: Around-mean-field
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At the end of the calculation, we can save the Greens function and self energy into a file::
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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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ar = HDFArchive("YourDFTDMFTcalculation.h5",'w')
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ar["G"] = S.G_iw
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ar["Sigma"] = S.Sigma_iw
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This is it!
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These are the essential steps to do a one-shot DFT+DMFT calculation. For full charge-self consistent calculations, there are some more things
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to consider, which we will see later on.
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