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https://github.com/triqs/dft_tools
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153 lines
6.3 KiB
ReStructuredText
153 lines
6.3 KiB
ReStructuredText
.. _advanced:
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A more advanced example
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=======================
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Normally, one wants to adjust some more parameters in order to make the calculation more efficient. Here, we
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will see a more advanced example, which is also suited for parallel execution.
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First, we load the necessary modules::
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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.dft.solver_multiband import *
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from pytriqs.gf.local import *
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from pytriqs.archive import *
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Then we define some parameters::
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dft_filename='srvo3'
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U = 2.7
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J = 0.65
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beta = 40
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loops = 10 # Number of DMFT sc-loops
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mix = 0.8 # Mixing factor of Sigma after solution of the AIM
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Delta_mix = 1.0 # Mixing factor of Delta as input for the AIM
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dc_type = 1 # DC type: 0 FLL, 1 Held, 2 AMF
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use_blocks = True # use bloc structure from DFT input
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use_matrix = False # True: Slater parameters, False: Kanamori parameters U+2J, U, U-J
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use_spinflip = False # use the full rotational invariant interaction?
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prec_mu = 0.0001
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qmc_cycles = 20000
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length_cycle = 200
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warming_iterations = 2000
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Most of these parameters are self-explaining. The first, `dft_filename`, gives the filename of the input files.
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The next step, as described in the previous section, is to convert the input files::
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Converter = Wien2kConverter(filename=dft_filename, repacking=True)
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Converter.convert_dft_input()
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mpi.barrier()
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The command ``mpi.barrier()`` ensures that all nodes wait until the conversion of the input is finished on the master
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node. After the conversion, we can check in the hdf5 archive, if previous runs are present, or if we have to start
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from scratch::
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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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ar = HDFArchive(dft_filename+'.h5','a')
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if 'iterations' in ar:
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previous_present = True
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previous_runs = ar['iterations']
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del ar
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previous_runs = mpi.bcast(previous_runs)
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previous_present = mpi.bcast(previous_present)
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# if previous runs are present, no need for recalculating the bloc structure:
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calc_blocs = use_blocks and (not previous_present)
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Now we can use all this information to initialise the :class:`SumkDFT` class::
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SK=SumkDFT(hdf_file=dft_filename+'.h5',use_dft_blocks=calc_blocs)
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If there was a previous run, we know already about the block structure, and therefore `UseDFTBlocs` is set to `False`.
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The next step is to initialise the Solver::
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Norb = SK.corr_shells[0]['dim']
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l = SK.corr_shells[0]['l']
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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:`SumkDFT` class, so we don't have
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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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if (previous_present):
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if (mpi.is_master_node()):
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ar = HDFArchive(dft_filename+'.h5','a')
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S.Sigma << ar['SigmaImFreq']
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del ar
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S.Sigma = mpi.bcast(S.Sigma)
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The last command is the broadcasting of the self energy from the master node to the slave nodes.
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Now we can go to the definition of the self-consistency step. It consists again of the basic steps discussed in the
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previous section, with some additional refinement::
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for iteration_number in range(1,loops+1) :
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SK.symm_deg_gf(S.Sigma,orb=0) # symmetrise Sigma
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SK.put_Sigma(Sigma_imp = [ S.Sigma ]) # put Sigma into the SumK class:
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chemical_potential = SK.calc_mu( precision = prec_mu ) # find the chemical potential
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S.G << SK.extract_G_loc()[0] # calculation of the local Green function
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mpi.report("Total charge of Gloc : %.6f"%S.G.total_density())
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if ((iteration_number==1)and(previous_present==False)):
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# Init the DC term and the real part of Sigma, if no previous run was found:
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dm = S.G.density()
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SK.calc_dc( dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type)
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S.Sigma << SK.dc_imp[0]['up'][0,0]
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S.G0 << inverse(S.Sigma + inverse(S.G))
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# Solve the impurity problem:
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S.solve(U_interact=U,J_hund=J,use_spinflip=use_spinflip,use_matrix=use_matrix,
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l=l,T=SK.T[0], dim_reps=SK.dim_reps[0], irep=2, n_cycles=qmc_cycles,
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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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S.Sigma <<(inverse(S.G0)-inverse(S.G))
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# Solve the impurity problem:
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S.solve(U_interact=U,J_hund=J,use_spinflip=use_spinflip,use_matrix=use_matrix,
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l=l,T=SK.T[0], dim_reps=SK.dim_reps[0], irep=2, n_cycles=qmc_cycles,
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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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S.Sigma <<(inverse(S.G0)-inverse(S.G))
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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()):
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ar = HDFArchive(dft_filename+'.h5','a')
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mpi.report("Mixing Sigma and G with factor %s"%mix)
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S.Sigma << mix * S.Sigma + (1.0-mix) * ar['Sigma']
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S.G << mix * S.G + (1.0-mix) * ar['GF']
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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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# Write the final Sigma and G to the hdf5 archive:
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if (mpi.is_master_node()):
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ar = HDFArchive(dft_filename+'.h5','a')
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ar['iterations'] = previous_runs + iteration_number
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ar['Sigma'] = S.Sigma
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ar['GF'] = S.G
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del ar
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# Now set new double counting:
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dm = S.G.density()
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SK.calc_dc( dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type)
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#Save stuff:
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SK.save(['chemical_potential','dc_imp','dc_energ'])
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This is all we need for the DFT+DMFT calculation. At the end, all results are stored in the hdf5 output file.
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