mirror of https://github.com/triqs/dft_tools
172 lines
6.2 KiB
Python
172 lines
6.2 KiB
Python
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.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 = 5 # Number of DMFT sc-loops
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Mix = 0.7 # Mixing factor in QMC
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DC_type = 0 # 0...FLL, 1...Held, 2... AMF, 3...Lichtenstein
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DC_Mix = 1.0 # 1.0 ... all from imp; 0.0 ... all from Gloc
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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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chemical_potential_init=0.0 # initial chemical potential
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HDFfilename = lda_filename+'.h5'
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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_dft_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=SumkDFT(hdf_file=lda_filename+'.h5',use_dft_blocks=False)
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Norb = SK.corr_shells[0]['dim']
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l = SK.corr_shells[0]['l']
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# Init the Hubbard-I solver:
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S = Solver(beta = beta, l = l)
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chemical_potential=chemical_potential_init
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# load previous data: old self-energy, chemical potential, DC correction
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if (previous_present):
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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['SigmaF']
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del ar
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things_to_load=['chemical_potential','dc_imp']
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old_data=SK.load(things_to_load)
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chemical_potential=old_data[0]
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SK.dc_imp=old_data[1]
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S.Sigma = mpi.bcast(S.Sigma)
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chemical_potential=mpi.bcast(chemical_potential)
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SK.dc_imp=mpi.bcast(SK.dc_imp)
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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 > 1):
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chemical_potential = SK.calc_mu( precision = 0.000001 )
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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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# calculated DC at the first run to have reasonable initial non-interacting atomic level positions
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if ((Iteration_Number==1)and(previous_present==False)):
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dc_value_init=U_int/2.0
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dm=S.G.density()
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SK.calc_dc( dm, U_interact = U_int, J_hund = J_hund, orb = 0, use_dc_formula = DC_type, use_dc_value=dc_value_init)
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# calculate non-interacting atomic level positions:
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eal = SK.eff_atomic_levels()[0]
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S.set_atomic_levels( eal = eal )
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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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# 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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ar = HDFArchive(HDFfilename,'r')
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mpi.report("Mixing Sigma and G with factor %s"%Mix)
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if ('SigmaF' in ar):
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S.Sigma <<= Mix * S.Sigma + (1.0-Mix) * ar['SigmaF']
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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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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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# after the Solver has finished, set new double counting:
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dm = S.G.density()
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SK.calc_dc( dm, U_interact = U_int, J_hund = J_hund, orb = 0, use_dc_formula = DC_type )
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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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# store the impurity self-energy, GF as well as correlation energy in h5
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if (mpi.is_master_node()):
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ar = HDFArchive(HDFfilename,'a')
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ar['iterations'] = itn
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ar['chemical_cotential%s'%itn] = chemical_potential
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ar['SigmaF'] = S.Sigma
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ar['GF'] = S.G
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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 essential SumkDFT data:
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things_to_save=['chemical_potential','dc_energ','dc_imp']
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SK.save(things_to_save)
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if (mpi.is_master_node()):
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print 'DC after solver: ',SK.dc_imp[0]
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# print out occupancy matrix of Ce 4f
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mpi.report("Orbital densities of impurity Green function:")
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for s in dm:
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mpi.report("Block %s: "%s)
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for ii in range(len(dm[s])):
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str = ''
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for jj in range(len(dm[s])):
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if (dm[s][ii,jj].real>0):
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str += " %.4f"%(dm[s][ii,jj].real)
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else:
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str += " %.4f"%(dm[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.calc_mu( precision = 0.000001 )
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# calculate and save occupancy matrix in the Bloch basis for Wien2k charge denity recalculation
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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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# store correlation energy contribution to be read by Wien2ki and then included to DFT+DMFT total 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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