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https://github.com/triqs/dft_tools
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More changes to template file.
This commit is contained in:
Priyanka Seth
parent
54827163ce
commit
59837c68b2
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@ -16,12 +16,11 @@ delta_mix = 1.0 # Mixing factor of Delta as input for the AIM
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dc_type = 0 # 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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prec_mu = 0.0001
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h_field = 0.0
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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_name"] = ""
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p["random_seed"] = 123 * mpi.rank + 567
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p["length_cycle"] = 50
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p["n_warmup_cycles"] = 50
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p["n_cycles"] = 5000
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@ -44,94 +43,99 @@ if mpi.is_master_node():
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del f
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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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SK=SumkDFT(hdf_file=dft_filename+'.h5',use_dft_blocks=calc_blocs)
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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 = ["%s"%i for i in range(num_orbitals)]
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orb_hybridized = False
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orb_names = [i for i in range(n_orb)]
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# Construct U matrix for density-density calculations
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gf_struct = set_operator_structure(spin_names,orb_names,orb_hybridized)
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# Use GF structure determined by DFT blocks
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gf_struct = SK.gf_struct_solver[0]
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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 Hamiltonian and solver
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H = h_loc_density(spin_names, orb_names, orb_hybridized, U=Umat, Uprime=Upmat, H_dump="H.txt")
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h_loc = h_loc_density(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U=Umat, Uprime=Upmat, H_dump="H.txt")
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S = Solver(beta=beta, gf_struct=gf_struct)
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if (previous_present):
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if (mpi.is_master_node()):
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if previous_present:
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if mpi.is_master_node():
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S.Sigma_iw << HDFArchive(dft_filename+'.h5','a')['dmft_output']['Sigma_iw']
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S.Sigma_iw = mpi.bcast(S.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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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 = ", i
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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.put_Sigma(Sigma_imp = [ S.Sigma_iw ]) # put Sigma into the SumK class
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chemical_potential = SK.calc_mu( precision = prec_mu ) # find 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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mpi.report("Total charge of Gloc : %.6f"%S.G_iw.total_density())
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SK.symm_deg_gf(S.Sigma_iw,orb=0) # symmetrise Sigma
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SK.put_Sigma(Sigma_imp = [ S.Sigma_iw ]) # put 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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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_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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# 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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# now calculate new G0_iw to input into the solver:
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if (mpi.is_master_node()):
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# We can do a mixing of Delta in order to stabilize the DMFT iterations:
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S.G0_iw << S.Sigma_iw + inverse(S.G_iw)
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ar = HDFArchive(dft_filename+'.h5','a')['dmft_output']
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if ((iteration_number>1) or (previous_present)):
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mpi.report("Mixing input Delta with factor %s"%delta_mix)
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Delta = (delta_mix * delta(S.G0_iw)) + (1.0-delta_mix) * ar['Delta_iw']
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S.G0_iw << S.G0_iw + delta(S.G0_iw) - Delta
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# Calculate new G0_iw to input into the solver:
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if mpi.is_master_node():
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# We can do a mixing of Delta in order to stabilize the DMFT iterations:
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S.G0_iw << S.Sigma_iw + inverse(S.G_iw)
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ar = HDFArchive(dft_filename+'.h5','a')['dmft_output']
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if (iteration_number>1 or previous_present):
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mpi.report("Mixing input Delta with factor %s"%delta_mix)
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Delta = (delta_mix * delta(S.G0_iw)) + (1.0-delta_mix) * ar['Delta_iw']
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S.G0_iw << S.G0_iw + delta(S.G0_iw) - Delta
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ar['Delta_iw'] = delta(S.G0_iw)
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S.G0_iw << inverse(S.G0_iw)
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del ar
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ar['Delta_iw'] = delta(S.G0_iw)
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S.G0_iw << inverse(S.G0_iw)
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del ar
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S.G0_iw << mpi.bcast(S.G0_iw)
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S.G0_iw = mpi.bcast(S.G0_iw)
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# Solve the impurity problem:
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S.solve(h_loc=h_loc, **p)
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# Solve the impurity problem:
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S.solve(h_loc=h_loc, **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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# solution done, do the 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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ar = HDFArchive(dft_filename+'.h5','a')['dmft_output']
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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['Sigma_iw']
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S.G_iw << sigma_mix * S.G_iw + (1.0-sigma_mix) * ar['G_iw']
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del ar
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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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# 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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ar = HDFArchive(dft_filename+'.h5','a')['dmft_output']
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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['Sigma_iw']
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S.G_iw << sigma_mix * S.G_iw + (1.0-sigma_mix) * ar['G_iw']
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del ar
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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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ar = HDFArchive(dft_filename+'.h5','a')['dmft_output']
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if previous_runs: iteration_number += previous_runs
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ar['iterations'] = iteration_number
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ar['G_tau'] = S.G_tau
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ar['G_iw'] = S.G_iw
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ar['Sigma_iw'] = S.Sigma_iw
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ar['G0-%s'%(iteration_number)] = S.G0_iw
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ar['G-%s'%(iteration_number)] = S.G_iw
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ar['Sigma-%s'%(iteration_number)] = S.Sigma_iw
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del ar
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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_iw'] = S.Sigma_iw
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ar['G_iw'] = S.G_iw
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del ar
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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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dm = S.G_iw.density() # compute the density matrix of the impurity problem
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# Set the double counting
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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 dft_output group of hdf5 archive in case of rerun:
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SK.save(['chemical_potential','dc_imp','dc_energ'])
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# Save stuff into the dft_output group of hdf5 archive in case of rerun:
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SK.save(['chemical_potential','dc_imp','dc_energ'])
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if mpi.is_master_node():
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ar = HDFArchive("dftdmft.h5",'w')
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ar["G_tau"] = S.G_tau
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ar["G_iw"] = S.G_iw
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ar["Sigma_iw"] = S.Sigma_iw
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