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uncompleted work on csc NiO tutorial
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@ -11,7 +11,7 @@ from triqs_cthyb import *
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import warnings
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warnings.filterwarnings("ignore", category=FutureWarning)
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filename = 'vasp'
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filename = 'nio'
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SK = SumkDFT(hdf_file = filename+'.h5', use_dft_blocks = False)
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beta = 5.0
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@ -2,7 +2,7 @@ from pytriqs.gf import *
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from pytriqs.archive import *
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from triqs_maxent import *
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filename = 'vasp'
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filename = 'nio'
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ar = HDFArchive(filename+'.h5','a')
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if 'iteration_count' in ar['DMFT_results']:
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@ -15,7 +15,7 @@ import triqs_dft_tools.version as dft_tools_version
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import warnings
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warnings.filterwarnings("ignore", category=FutureWarning)
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filename = 'vasp'
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filename = 'nio'
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SK = SumkDFT(hdf_file = filename+'.h5', use_dft_blocks = False)
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201
doc/tutorials/images_scripts/nio_csc.py
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doc/tutorials/images_scripts/nio_csc.py
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from itertools import *
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import numpy as np
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import pytriqs.utility.mpi as mpi
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from pytriqs.archive import *
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from pytriqs.gf import *
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import sys, pytriqs.version as triqs_version
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from triqs_dft_tools.sumk_dft import *
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from triqs_dft_tools.sumk_dft_tools import *
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from pytriqs.operators.util.hamiltonians import *
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from pytriqs.operators.util.U_matrix import *
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from triqs_cthyb import *
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import triqs_cthyb.version as cthyb_version
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import triqs_dft_tools.version as dft_tools_version
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from triqs_dft_tools.converters.vasp_converter import *
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import warnings
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warnings.filterwarnings("ignore", category=FutureWarning)
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def dmft_cycle():
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filename = 'nio'
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Converter = VaspConverter(filename=filename)
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Converter.convert_dft_input()
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SK = SumkDFT(hdf_file = filename+'.h5', use_dft_blocks = False)
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beta = 5.0
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Sigma = SK.block_structure.create_gf(beta=beta)
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SK.put_Sigma([Sigma])
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G = SK.extract_G_loc()
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SK.analyse_block_structure_from_gf(G, threshold = 1e-2)
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for i_sh in range(len(SK.deg_shells)):
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num_block_deg_orbs = len(SK.deg_shells[i_sh])
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mpi.report('found {0:d} blocks of degenerate orbitals in shell {1:d}'.format(num_block_deg_orbs, i_sh))
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for iblock in range(num_block_deg_orbs):
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mpi.report('block {0:d} consists of orbitals:'.format(iblock))
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for keys in SK.deg_shells[i_sh][iblock].keys():
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mpi.report(' '+keys)
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# Setup CTQMC Solver
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n_orb = SK.corr_shells[0]['dim']
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spin_names = ['up','down']
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orb_names = [i for i in range(0,n_orb)]
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#gf_struct = set_operator_structure(spin_names, orb_names, orb_hyb)
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gf_struct = SK.gf_struct_solver[0]
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mpi.report('Sumk to Solver: %s'%SK.sumk_to_solver)
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mpi.report('GF struct sumk: %s'%SK.gf_struct_sumk)
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mpi.report('GF struct solver: %s'%SK.gf_struct_solver)
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S = Solver(beta=beta, gf_struct=gf_struct)
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# Construct the Hamiltonian and save it in Hamiltonian_store.txt
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H = Operator()
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U = 8.0
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J = 1.0
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U_sph = U_matrix(l=2, U_int=U, J_hund=J)
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U_cubic = transform_U_matrix(U_sph, spherical_to_cubic(l=2, convention=''))
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Umat, Upmat = reduce_4index_to_2index(U_cubic)
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H = h_int_density(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U=Umat, Uprime=Upmat)
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# Print some information on the master node
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mpi.report('Greens function structure is: %s '%gf_struct)
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mpi.report('U Matrix set to:\n%s'%Umat)
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mpi.report('Up Matrix set to:\n%s'%Upmat)
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# Parameters for the CTQMC Solver
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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"] = 100
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p["n_warmup_cycles"] = 2000
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p["n_cycles"] = 20000
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p["fit_max_moment"] = 4
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p["fit_min_n"] = 30
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p["fit_max_n"] = 50
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p["perform_tail_fit"] = True
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# Double Counting: 0 FLL, 1 Held, 2 AMF
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DC_type = 0
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DC_value = 59.0
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# Prepare hdf file and and check for previous iterations
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n_iterations = 1
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iteration_offset = 0
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if mpi.is_master_node():
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ar = HDFArchive(filename+'.h5','a')
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if not 'DMFT_results' in ar: ar.create_group('DMFT_results')
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if not 'Iterations' in ar['DMFT_results']: ar['DMFT_results'].create_group('Iterations')
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if not 'DMFT_input' in ar: ar.create_group('DMFT_input')
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if not 'Iterations' in ar['DMFT_input']: ar['DMFT_input'].create_group('Iterations')
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if not 'code_versions' in ar['DMFT_input']: ar['DMFT_input'].create_group('code_versio\
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ns')
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ar['DMFT_input']['code_versions']["triqs_version"] = triqs_version.version
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ar['DMFT_input']['code_versions']["triqs_git"] = triqs_version.git_hash
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ar['DMFT_input']['code_versions']["cthyb_version"] = cthyb_version.version
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ar['DMFT_input']['code_versions']["cthyb_git"] = cthyb_version.cthyb_hash
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ar['DMFT_input']['code_versions']["dft_tools_version"] = dft_tools_version.version
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ar['DMFT_input']['code_versions']["dft_tools_version"] = dft_tools_version.dft_tools_hash
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if 'iteration_count' in ar['DMFT_results']:
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iteration_offset = ar['DMFT_results']['iteration_count']+1
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S.Sigma_iw = ar['DMFT_results']['Iterations']['Sigma_it'+str(iteration_offset-1)]
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SK.dc_imp = ar['DMFT_results']['Iterations']['dc_imp'+str(iteration_offset-1)]
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SK.dc_energ = ar['DMFT_results']['Iterations']['dc_energ'+str(iteration_offset-1)]
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SK.chemical_potential = ar['DMFT_results']['Iterations']['chemical_potential'+str(iteration_offset-1)].real
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ar['DMFT_input']["dmft_script_it"+str(iteration_offset)] = open(sys.argv[0]).read()
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iteration_offset = mpi.bcast(iteration_offset)
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S.Sigma_iw = mpi.bcast(S.Sigma_iw)
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SK.dc_imp = mpi.bcast(SK.dc_imp)
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SK.dc_energ = mpi.bcast(SK.dc_energ)
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SK.chemical_potential = mpi.bcast(SK.chemical_potential)
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# Calc the first G0
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SK.symm_deg_gf(S.Sigma_iw,orb=0)
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SK.put_Sigma(Sigma_imp = [S.Sigma_iw])
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SK.calc_mu(precision=0.01)
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S.G_iw << SK.extract_G_loc()[0]
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SK.symm_deg_gf(S.G_iw, orb=0)
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#Init the DC term and the self-energy if no previous iteration was found
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if iteration_offset == 0:
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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,use_dc_value=DC_value)
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S.Sigma_iw << SK.dc_imp[0]['up'][0,0]
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mpi.report('%s DMFT cycles requested. Starting with iteration %s.'%(n_iterations,iteration_offset))
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# The infamous DMFT self consistency cycle
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for it in range(iteration_offset, iteration_offset + n_iterations):
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mpi.report('Doing iteration: %s'%it)
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# Get G0
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S.G0_iw << inverse(S.Sigma_iw + inverse(S.G_iw))
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# Solve the impurity problem
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S.solve(h_int = H, **p)
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if mpi.is_master_node():
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ar['DMFT_input']['Iterations']['solver_dict_it'+str(it)] = p
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ar['DMFT_results']['Iterations']['Gimp_it'+str(it)] = S.G_iw
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ar['DMFT_results']['Iterations']['Gtau_it'+str(it)] = S.G_tau
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ar['DMFT_results']['Iterations']['Sigma_uns_it'+str(it)] = S.Sigma_iw
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# Calculate double counting
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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,use_dc_value=DC_value)
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# Get new G
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SK.symm_deg_gf(S.Sigma_iw,orb=0)
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SK.put_Sigma(Sigma_imp=[S.Sigma_iw])
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SK.calc_mu(precision=0.01)
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S.G_iw << SK.extract_G_loc()[0]
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# print densities
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for sig,gf in S.G_iw:
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mpi.report("Orbital %s density: %.6f"%(sig,dm[sig][0,0]))
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mpi.report('Total charge of Gloc : %.6f'%S.G_iw.total_density())
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if mpi.is_master_node():
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ar['DMFT_results']['iteration_count'] = it
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ar['DMFT_results']['Iterations']['Sigma_it'+str(it)] = S.Sigma_iw
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ar['DMFT_results']['Iterations']['Gloc_it'+str(it)] = S.G_iw
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ar['DMFT_results']['Iterations']['G0loc_it'+str(it)] = S.G0_iw
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ar['DMFT_results']['Iterations']['dc_imp'+str(it)] = SK.dc_imp
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ar['DMFT_results']['Iterations']['dc_energ'+str(it)] = SK.dc_energ
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ar['DMFT_results']['Iterations']['chemical_potential'+str(it)] = SK.chemical_potential
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if mpi.is_master_node():
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print 'calculating mu...'
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SK.chemical_potential = SK.calc_mu( precision = 0.000001 )
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if mpi.is_master_node():
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print 'calculating GAMMA'
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SK.calc_density_correction(dm_type='vasp')
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if mpi.is_master_node():
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print 'calculating energy corrections'
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correnerg = 0.5 * (S.G_iw * S.Sigma_iw).total_density()
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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,use_dc_value=DC_value)
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dc_energ = SK.dc_energ[0]
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if mpi.is_master_node():
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ar['DMFT_results']['Iterations']['corr_energy_it'+str(it)] = correnerg
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ar['DMFT_results']['Iterations']['dc_energy_it'+str(it)] = dc_energ
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if mpi.is_master_node(): del ar
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return correnerg, dc_energ
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doc/tutorials/images_scripts/nio_csc.py.rst
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doc/tutorials/images_scripts/nio_csc.py.rst
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.. _nio_csc.py:
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nio_csc.py
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-------------
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Download :download:`nio_csc.py <./nio_csc.py>`.
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.. literalinclude:: nio_csc.py
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:language: python
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@ -7,6 +7,7 @@ SHELLS = 1 2
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EWINDOW = -9 2
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NORMION = False
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NORMALIZE = True
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BANDS = 2 10
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[Shell 1] # Ni d shell
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LSHELL = 2
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DFT and projections
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==================================================
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We will perform charge self-consitent DFT+DMFT calcluations for the charge-transfer insulator NiO. We start from scratch and provide all necessary input files to do the calculations: First for doing a single-shot calculation and then for charge-selfconsistency.
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We will perform DFT+DMFT calcluations for the charge-transfer insulator NiO. We start from scratch and provide all necessary input files to do the calculations: First for doing a single-shot calculation.
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.. (and then for charge-selfconsistency).
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VASP setup
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-------------------------------
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@ -62,7 +63,7 @@ For sensible results run this script in parallel on at least 20 cores. As a quic
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Local lattice Green's function for all projected orbitals
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----------------------
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We calculate the local lattice Green's function - now also for the uncorrelated orbitals, i.e., the O p states, for what we use the script :ref:`NiO_local_lattice_GF.py`. The result is saved in the h5 file as `G_latt_orb_it<n_it>`, where `n_it>` is the number of the last DMFT iteration.
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We calculate the local lattice Green's function - now also for the uncorrelated orbitals, i.e., the O p states, for what we use the script :ref:`NiO_local_lattice_GF.py`. The result is saved in the h5 file as `G_latt_orb_it<n_it>`, where `<n_it>` is the number of the last DMFT iteration.
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Spectral function on real axis: MaxEnt
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----------------------
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@ -72,3 +73,27 @@ To compare to results from literature we make use of the `maxent triqs applicati
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.. image:: images_scripts/nio_Aw.png
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:width: 400
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:align: center
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.. Charge self-consistent DMFT
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.. ==================================================
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.. In this part we will perform charge self-consistent DMFT calculations. To do so we have to adapt the VASP `INCAR` such that :program:`VASP` reads the updated charge density after each step. We add the lines
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.. ICHARG = 5
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.. NELM = 1000
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.. NELMIN = 1000
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.. which makes VASP wait after each step of its iterative diagonalization until the file vasp.lock is created. It then reads the update of the charge density in the file `GAMMA`. It is terminated by an external script after a desired amount of steps, such that we deactivate all automatic stoping criterion by setting the number of steps to a very high number.
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.. We take the respective converged DFT and DMFT calculations from before as a starting point. I.e., we copy the `CHGCAR` and `nio.h5` together with the other :program:`VASP` input files and :file:`plo.cfg` in a new directory. We use a script called :program:`vasp_dmft` to invoke :program:`VASP` in the background and start the DMFT calculation together with :program:`plovasp` and the converter. This script assumes that the dmft sript contains a function `dmft_cycle()` and also the conversion from text files to the h5 file. Additionally we have to add a few lines to calculate the density correction and calculate the correlation energy. We adapt the script straightforardly and call it :ref:`nio_csc.py`. The most important new lines are
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.. SK.chemical_potential = SK.calc_mu( precision = 0.000001 )
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.. SK.calc_density_correction(dm_type='vasp')
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.. correnerg = 0.5 * (S.G_iw * S.Sigma_iw).total_density()
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.. where the chemical potential is determined to a greater precision than before, the correction to the dft density matrix is calculated and output to the file :file:`GAMMA`. The correlation energy is calculated via Migdal-Galitzki formula. We also slightly increase the tolerance for the detection of blocks since the DFT calculation now includes some QMC noise.
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.. We can start the whole machinery by excectuing
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.. vasp_dmft -n <n_procs> -i <n_iters> -p <vasp_exec> nio_csc.py
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