from pytriqs.applications.dft.sumk_lda import * from pytriqs.applications.dft.converters.wien2k_converter import * from pytriqs.applications.impurity_solvers.hubbard_I.hubbard_solver import Solver lda_filename = 'Ce-gamma' beta = 40 U_int = 6.00 J_hund = 0.70 Loops = 2 # Number of DMFT sc-loops Mix = 0.7 # Mixing factor in QMC # 1.0 ... all from imp; 0.0 ... all from Gloc DC_type = 0 # 0...FLL, 1...Held, 2... AMF, 3...Lichtenstein useBlocs = False # use bloc structure from LDA input useMatrix = True # use the U matrix calculated from Slater coefficients instead of (U+2J, U, U-J) Natomic = 1 HDFfilename = lda_filename+'.h5' use_val= U_int * (Natomic - 0.5) - J_hund * (Natomic * 0.5 - 0.5) # Convert DMFT input: # Can be commented after the first run Converter = Wien2kConverter(filename=lda_filename) Converter.convert_dmft_input() #check if there are previous runs: previous_runs = 0 previous_present = False if mpi.is_master_node(): ar = HDFArchive(HDFfilename,'a') if 'iterations' in ar: previous_present = True previous_runs = ar['iterations'] else: previous_runs = 0 previous_present = False del ar mpi.barrier() previous_runs = mpi.bcast(previous_runs) previous_present = mpi.bcast(previous_present) # Init the SumK class SK=SumkLDA(hdf_file=lda_filename+'.h5',use_lda_blocks=False) Norb = SK.corr_shells[0][3] l = SK.corr_shells[0][2] # Init the Solver: S = Solver(beta = beta, l = l) if (previous_present): # load previous data: mpi.report("Using stored data for initialisation") if (mpi.is_master_node()): ar = HDFArchive(HDFfilename,'a') S.Sigma << ar['SigmaImFreq'] del ar S.Sigma = mpi.bcast(S.Sigma) SK.load() # DMFT loop: for Iteration_Number in range(1,Loops+1): itn = Iteration_Number + previous_runs # put Sigma into the SumK class: SK.put_Sigma(Sigma_imp = [ S.Sigma ]) # Compute the SumK, possibly fixing mu by dichotomy if SK.density_required and (Iteration_Number > 0): Chemical_potential = SK.find_mu( precision = 0.01 ) else: mpi.report("No adjustment of chemical potential\nTotal density = %.3f"%SK.total_density(mu=Chemical_potential)) # Density: S.G << SK.extract_G_loc()[0] mpi.report("Total charge of Gloc : %.6f"%S.G.total_density()) dm = S.G.density() if ((Iteration_Number==1)and(previous_present==False)): 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) # set atomic levels: eal = SK.eff_atomic_levels()[0] S.set_atomic_levels( eal = eal ) # update hdf5 if (mpi.is_master_node()): ar = HDFArchive(HDFfilename,'a') ar['Chemical_Potential%s'%itn] = Chemical_potential del ar # solve it: S.solve(U_int = U_int, J_hund = J_hund, verbosity = 1) if (mpi.is_master_node()): ar = HDFArchive(HDFfilename) ar['iterations'] = itn # Now mix Sigma and G: if ((itn>1)or(previous_present)): if (mpi.is_master_node()and (Mix<1.0)): mpi.report("Mixing Sigma and G with factor %s"%Mix) if ('SigmaImFreq' in ar): S.Sigma << Mix * S.Sigma + (1.0-Mix) * ar['SigmaImFreq'] if ('GF' in ar): S.G << Mix * S.G + (1.0-Mix) * ar['GF'] S.G = mpi.bcast(S.G) S.Sigma = mpi.bcast(S.Sigma) if (mpi.is_master_node()): ar['SigmaImFreq'] = S.Sigma ar['GF'] = S.G # after the Solver has finished, set new double counting: dm = S.G.density() SK.set_dc( dm, U_interact = U_int, J_hund = J_hund, orb = 0, use_dc_formula = DC_type , use_val=use_val) # correlation energy calculations: correnerg = 0.5 * (S.G * S.Sigma).total_density() mpi.report("Corr. energy = %s"%correnerg) if (mpi.is_master_node()): ar['correnerg%s'%itn] = correnerg ar['DCenerg%s'%itn] = SK.dc_energ del ar #Save stuff: SK.save() if (mpi.is_master_node()): print 'DC after solver: ',SK.dc_imp[SK.invshellmap[0]] # do some analysis: mpi.report("Orbital densities of impurity Green function:") dm1 = S.G.density() for s in dm1: mpi.report("Block %s: "%s) for ii in range(len(dm1[s])): str = '' for jj in range(len(dm1[s])): if (dm1[s][ii,jj].real>0): str += " %.4f"%(dm1[s][ii,jj].real) else: str += " %.4f"%(dm1[s][ii,jj].real) mpi.report(str) mpi.report("Total charge of impurity problem : %.6f"%S.G.total_density()) # find exact chemical potential if (SK.density_required): SK.chemical_potential = SK.find_mu( precision = 0.000001 ) dN,d = SK.calc_density_correction(filename = lda_filename+'.qdmft') mpi.report("Trace of Density Matrix: %s"%d) #correlation energy: if (mpi.is_master_node()): ar = HDFArchive(HDFfilename) itn = ar['iterations'] correnerg = ar['correnerg%s'%itn] DCenerg = ar['DCenerg%s'%itn] del ar correnerg -= DCenerg[0] f=open(lda_filename+'.qdmft','a') f.write("%.16f\n"%correnerg) f.close()