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![Priyanka Seth](/assets/img/avatar_default.png)
* Replace <<= with << * Reordering in put_Sigma * Remove all instances of wien2triqs in doc
172 lines
5.5 KiB
Python
172 lines
5.5 KiB
Python
from pytriqs.applications.dft.sumk_lda import *
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from pytriqs.applications.dft.converters.wien2k_converter import *
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from pytriqs.applications.impurity_solvers.hubbard_I.hubbard_solver import Solver
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lda_filename = 'Ce-gamma'
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beta = 40
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U_int = 6.00
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J_hund = 0.70
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Loops = 2 # Number of DMFT sc-loops
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Mix = 0.7 # Mixing factor in QMC
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# 1.0 ... all from imp; 0.0 ... all from Gloc
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DC_type = 0 # 0...FLL, 1...Held, 2... AMF, 3...Lichtenstein
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useBlocs = False # use bloc structure from LDA input
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useMatrix = True # use the U matrix calculated from Slater coefficients instead of (U+2J, U, U-J)
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Natomic = 1
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HDFfilename = lda_filename+'.h5'
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use_val= U_int * (Natomic - 0.5) - J_hund * (Natomic * 0.5 - 0.5)
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# Convert DMFT input:
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# Can be commented after the first run
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Converter = Wien2kConverter(filename=lda_filename)
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Converter.convert_dmft_input()
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#check if there are previous runs:
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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(HDFfilename,'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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else:
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previous_runs = 0
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previous_present = False
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del ar
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mpi.barrier()
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previous_runs = mpi.bcast(previous_runs)
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previous_present = mpi.bcast(previous_present)
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# Init the SumK class
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SK=SumkLDA(hdf_file=lda_filename+'.h5',use_lda_blocks=False)
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Norb = SK.corr_shells[0][3]
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l = SK.corr_shells[0][2]
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# Init the Solver:
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S = Solver(beta = beta, l = l)
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if (previous_present):
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# load previous data:
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mpi.report("Using stored data for initialisation")
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if (mpi.is_master_node()):
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ar = HDFArchive(HDFfilename,'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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SK.load()
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# DMFT loop:
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for Iteration_Number in range(1,Loops+1):
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itn = Iteration_Number + previous_runs
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# put Sigma into the SumK class:
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SK.put_Sigma(Sigma_imp = [ S.Sigma ])
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# Compute the SumK, possibly fixing mu by dichotomy
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if SK.density_required and (Iteration_Number > 0):
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Chemical_potential = SK.find_mu( precision = 0.01 )
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else:
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mpi.report("No adjustment of chemical potential\nTotal density = %.3f"%SK.total_density(mu=Chemical_potential))
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# Density:
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S.G << SK.extract_G_loc()[0]
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mpi.report("Total charge of Gloc : %.6f"%S.G.total_density())
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dm = S.G.density()
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if ((Iteration_Number==1)and(previous_present==False)):
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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)
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# set atomic levels:
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eal = SK.eff_atomic_levels()[0]
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S.set_atomic_levels( eal = eal )
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# update hdf5
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if (mpi.is_master_node()):
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ar = HDFArchive(HDFfilename,'a')
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ar['Chemical_Potential%s'%itn] = Chemical_potential
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del ar
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# solve it:
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S.solve(U_int = U_int, J_hund = J_hund, verbosity = 1)
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if (mpi.is_master_node()):
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ar = HDFArchive(HDFfilename)
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ar['iterations'] = itn
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# Now mix Sigma and G:
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if ((itn>1)or(previous_present)):
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if (mpi.is_master_node()and (Mix<1.0)):
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mpi.report("Mixing Sigma and G with factor %s"%Mix)
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if ('SigmaImFreq' in ar):
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S.Sigma << Mix * S.Sigma + (1.0-Mix) * ar['SigmaImFreq']
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if ('GF' in ar):
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S.G << Mix * S.G + (1.0-Mix) * ar['GF']
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S.G = mpi.bcast(S.G)
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S.Sigma = mpi.bcast(S.Sigma)
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if (mpi.is_master_node()):
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ar['SigmaImFreq'] = S.Sigma
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ar['GF'] = S.G
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# after the Solver has finished, set new double counting:
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dm = S.G.density()
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SK.set_dc( dm, U_interact = U_int, J_hund = J_hund, orb = 0, use_dc_formula = DC_type , use_val=use_val)
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# correlation energy calculations:
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correnerg = 0.5 * (S.G * S.Sigma).total_density()
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mpi.report("Corr. energy = %s"%correnerg)
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if (mpi.is_master_node()):
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ar['correnerg%s'%itn] = correnerg
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ar['DCenerg%s'%itn] = SK.dc_energ
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del ar
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#Save stuff:
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SK.save()
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if (mpi.is_master_node()):
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print 'DC after solver: ',SK.dc_imp[SK.invshellmap[0]]
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# do some analysis:
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mpi.report("Orbital densities of impurity Green function:")
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dm1 = S.G.density()
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for s in dm1:
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mpi.report("Block %s: "%s)
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for ii in range(len(dm1[s])):
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str = ''
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for jj in range(len(dm1[s])):
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if (dm1[s][ii,jj].real>0):
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str += " %.4f"%(dm1[s][ii,jj].real)
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else:
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str += " %.4f"%(dm1[s][ii,jj].real)
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mpi.report(str)
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mpi.report("Total charge of impurity problem : %.6f"%S.G.total_density())
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# find exact chemical potential
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if (SK.density_required):
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SK.chemical_potential = SK.find_mu( precision = 0.000001 )
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dN,d = SK.calc_density_correction(filename = lda_filename+'.qdmft')
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mpi.report("Trace of Density Matrix: %s"%d)
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#correlation energy:
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if (mpi.is_master_node()):
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ar = HDFArchive(HDFfilename)
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itn = ar['iterations']
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correnerg = ar['correnerg%s'%itn]
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DCenerg = ar['DCenerg%s'%itn]
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del ar
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correnerg -= DCenerg[0]
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f=open(lda_filename+'.qdmft','a')
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f.write("%.16f\n"%correnerg)
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f.close()
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