mirror of
https://github.com/triqs/dft_tools
synced 2024-11-18 20:12:53 +01:00
a1209f8a53
* adapted all occurences of the converter script file names including the doc files * fixed one failing test: Py_basis_transformation.py
151 lines
5.8 KiB
Python
151 lines
5.8 KiB
Python
import triqs.utility.mpi as mpi
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from triqs.operators.util import *
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from h5 import HDFArchive
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from triqs_cthyb import *
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from triqs.gf import *
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from triqs_dft_tools.sumk_dft import *
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from triqs_dft_tools.converters.wien2k import *
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dft_filename='SrVO3'
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beta = 40
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loops = 15 # Number of DMFT sc-loops
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sigma_mix = 1.0 # Mixing factor of Sigma after solution of the AIM
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use_blocks = True # use bloc structure from DFT input
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prec_mu = 0.0001
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h_field = 0.0
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## KANAMORI DENSITY-DENSITY (for full Kanamori use h_int_kanamori)
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# Define interaction paramters, DC and Hamiltonian
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U = 4.0
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J = 0.65
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dc_type = 1 # DC type: 0 FLL, 1 Held, 2 AMF
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# Construct U matrix for density-density calculations
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Umat, Upmat = U_matrix_kanamori(n_orb=n_orb, U_int=U, J_hund=J)
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# Construct density-density Hamiltonian
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h_int = h_int_density(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U=Umat, Uprime=Upmat)
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## SLATER HAMILTONIAN
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## Define interaction paramters, DC and Hamiltonian
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#U = 9.6
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#J = 0.8
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#dc_type = 0 # DC type: 0 FLL, 1 Held, 2 AMF
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## Construct Slater U matrix
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#U_sph = U_matrix(l=2, U_int=U, J_hund=J)
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#U_cubic = transform_U_matrix(U_sph, spherical_to_cubic(l=2, convention='wien2k'))
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#Umat = t2g_submatrix(U_cubic, convention='wien2k')
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## Construct Slater Hamiltonian
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#h_int = h_int_slater(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U_matrix=Umat)
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# Solver parameters
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p = {}
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p["max_time"] = -1
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p["random_seed"] = 123 * mpi.rank + 567
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p["length_cycle"] = 200
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p["n_warmup_cycles"] = 100000
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p["n_cycles"] = 1000000
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p["perform_tail_fit"] = True
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p["fit_max_moment"] = 4
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p["fit_min_n"] = 30
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p["fit_max_n"] = 60
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# If conversion step was not done, we could do it here. Uncomment the lines it you want to do this.
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#from triqs_dft_tools.converters.wien2k import *
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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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previous_runs = 0
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previous_present = False
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if mpi.is_master_node():
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with HDFArchive(dft_filename+'.h5','a') as f:
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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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previous_present = True
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previous_runs = ar['iterations']
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else:
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f.create_group('dmft_output')
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previous_runs = mpi.bcast(previous_runs)
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previous_present = mpi.bcast(previous_present)
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SK=SumkDFT(hdf_file=dft_filename+'.h5',use_dft_blocks=use_blocks,h_field=h_field)
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n_orb = SK.corr_shells[0]['dim']
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l = SK.corr_shells[0]['l']
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spin_names = ["up","down"]
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orb_names = [i for i in range(n_orb)]
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# Use GF structure determined by DFT blocks
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gf_struct = [(block, indices) for block, indices in SK.gf_struct_solver[0].items()]
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# Construct Solver
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S = Solver(beta=beta, gf_struct=gf_struct)
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if previous_present:
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chemical_potential = 0
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dc_imp = 0
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dc_energ = 0
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if mpi.is_master_node():
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with HDFArchive(dft_filename+'.h5','r') as ar:
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S.Sigma_iw << ar['dmft_output']['Sigma_iw']
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chemical_potential,dc_imp,dc_energ = SK.load(['chemical_potential','dc_imp','dc_energ'])
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S.Sigma_iw << mpi.bcast(S.Sigma_iw)
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chemical_potential = mpi.bcast(chemical_potential)
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dc_imp = mpi.bcast(dc_imp)
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dc_energ = mpi.bcast(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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for iteration_number in range(1,loops+1):
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if mpi.is_master_node(): print("Iteration = ", iteration_number)
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SK.symm_deg_gf(S.Sigma_iw,orb=0) # symmetrise Sigma
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SK.set_Sigma([ S.Sigma_iw ]) # set Sigma into the SumK class
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chemical_potential = SK.calc_mu( precision = prec_mu ) # find the chemical potential for given density
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S.G_iw << SK.extract_G_loc()[0] # calc the local Green function
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mpi.report("Total charge of Gloc : %.6f"%S.G_iw.total_density())
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# Init the DC term and the real part of Sigma, if no previous runs found:
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if (iteration_number==1 and previous_present==False):
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dm = S.G_iw.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_iw << SK.dc_imp[0]['up'][0,0]
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# Calculate new G0_iw to input into the solver:
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S.G0_iw << inverse(S.Sigma_iw + inverse(S.G_iw))
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# Solve the impurity problem:
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S.solve(h_int=h_int, **p)
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# Solved. Now do post-processing:
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mpi.report("Total charge of impurity problem : %.6f"%S.G_iw.total_density())
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# Now mix Sigma and G with factor sigma_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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with HDFArchive(dft_filename+'.h5','r') as ar:
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mpi.report("Mixing Sigma and G with factor %s"%sigma_mix)
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S.Sigma_iw << sigma_mix * S.Sigma_iw + (1.0-sigma_mix) * ar['dmft_output']['Sigma_iw']
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S.G_iw << sigma_mix * S.G_iw + (1.0-sigma_mix) * ar['dmft_output']['G_iw']
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S.G_iw << mpi.bcast(S.G_iw)
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S.Sigma_iw << mpi.bcast(S.Sigma_iw)
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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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with HDFArchive(dft_filename+'.h5','a') as ar:
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ar['dmft_output']['iterations'] = iteration_number + previous_runs
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ar['dmft_output']['G_tau'] = S.G_tau
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ar['dmft_output']['G_iw'] = S.G_iw
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ar['dmft_output']['Sigma_iw'] = S.Sigma_iw
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ar['dmft_output']['G0-%s'%(iteration_number)] = S.G0_iw
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ar['dmft_output']['G-%s'%(iteration_number)] = S.G_iw
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ar['dmft_output']['Sigma-%s'%(iteration_number)] = S.Sigma_iw
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# Set the new double counting:
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dm = S.G_iw.density() # compute the 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)
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# Save stuff into the user_data group of hdf5 archive in case of rerun:
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SK.save(['chemical_potential','dc_imp','dc_energ'])
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