mirror of
https://github.com/triqs/dft_tools
synced 2024-11-18 12:03:50 +01:00
f27399758e
merged unstable into py3
* include the major changes of a94f8ed942
of blockstructure.py
* adapted all code to python 3
* fixed all tests
113 lines
3.8 KiB
Python
113 lines
3.8 KiB
Python
# Import the modules:
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from triqs_dft_tools.sumk_dft import *
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from triqs.gf import *
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from h5 import HDFArchive
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from triqs.operators.util import *
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from triqs.operators.util.U_matrix import *
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from triqs_cthyb import *
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import triqs.utility.mpi as mpi
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# Init the SumK class:
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filename = 'Sr2MgOsO6_SOC.h5'
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SK = SumkDFT(hdf_file=filename,use_dft_blocks=True)
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# Find diagonal local basis set:
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SK.calculate_diagonalization_matrix(prop_to_be_diagonal='eal',calc_in_solver_blocks=True)
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###########################
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# Now we pick the orbitals:
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# BE CAREFUL: THIS NEEDS TO BE DONE PROPERLY
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# AND IS DIFFERENT FORM CASE TO CASE!
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SK.block_structure.pick_gf_struct_solver([{'ud_0': [0,1,2],'ud_1': [0,1,2]}])
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###########################
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# Now we set up the U matrix, first in cubic (Wien2k) convention:
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U = 2.0
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J = 0.2
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U_sph = U_matrix(l=2, U_int=U, J_hund=J)
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U_sph = np.kron(np.reshape(np.eye(2),(1,2,1,2)),np.kron(np.reshape(np.eye(2),(2,1,2,1)),U_sph))
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U_mat = transform_U_matrix(U_sph, SK.T[0].conjugate())
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# Now we set up the interaction Hamiltonian
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h_sumk = h_int_slater(['ud'], range(10), U_mat, off_diag=True, complex=True)
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# And convert it to the solver structure
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h_int = SK.block_structure.convert_operator(h_sumk)
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# Solver Init:
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beta = 40.0
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S = Solver(beta=beta, gf_struct=SK.block_structure.gf_struct_solver_list[0])
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# Solver parameters:
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p = {}
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# solver
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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"] = 3000000
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# tail fit
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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_w"] = 4.0
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p["fit_max_w"] = 8.0
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# double counting correction:
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dc_type = 0 # FLL
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# DMFT loops:
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n_loops = 1
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#for first iteration, add the outout group:
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if mpi.is_master_node():
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with HDFArchive(filename) as ar:
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if (not ar.is_group('dmft_output')):
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ar.create_group('dmft_output')
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for iteration_number in range(1,n_loops+1):
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mpi.report("Iteration = %s"%iteration_number)
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SK.set_Sigma([ S.Sigma_iw ]) # put Sigma into the SumK class
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chemical_potential = SK.calc_mu( precision = 0.01 ) # find the chemical potential for given density
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S.G_iw << SK.extract_G_loc()[0]
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if (iteration_number==1):
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# Put Hartree energy on Re Sigma
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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.block_structure.convert_matrix(SK.dc_imp[0],space_from='sumk',space_to='solver')['ud_0'][0,0]
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mpi.report("Orbital densities of local Green function :")
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for s,gf in S.G_iw:
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mpi.report("Orbital %s: %s"%(s,dm[s].real))
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mpi.report("Total charge of Gloc : %.6f"%S.G_iw.total_density().real)
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# Calculate new G0_iw to input into the solver:
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S.G0_iw << S.Sigma_iw + inverse(S.G_iw)
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S.G0_iw << inverse(S.G0_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-solution stuff:
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dm = S.G_iw.density()
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mpi.report("Orbital densities of impurity Green function:")
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for s,gf in S.G_iw:
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mpi.report("Orbital %s: %s"%(s,dm[s].real))
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mpi.report("Total charge of impurity problem : %.6f"%S.G_iw.total_density().real)
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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(filename) as ar:
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ar['dmft_output']['iterations'] = iteration_number
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ar['dmft_output']['G_0'] = S.G0_iw
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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'%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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