orb_names = [i for i in range(0, n_orb)]
#gf_struct = set_operator_structure(spin_names, orb_names, orb_hyb)
gf_struct = SK.gf_struct_solver[0]
mpi.report('Sumk to Solver: %s' % SK.sumk_to_solver)
mpi.report('GF struct sumk: %s' % SK.gf_struct_sumk)
mpi.report('GF struct solver: %s' % SK.gf_struct_solver)
S = Solver(beta=beta, gf_struct=gf_struct)
# Construct the Hamiltonian and save it in Hamiltonian_store.txt
H = Operator()
U = 8.0
J = 1.0
U_sph = U_matrix(l=2, U_int=U, J_hund=J)
U_cubic = transform_U_matrix(U_sph, spherical_to_cubic(l=2, convention=''))
Umat, Upmat = reduce_4index_to_2index(U_cubic)
H = h_int_density(spin_names,
orb_names,
map_operator_structure=SK.sumk_to_solver[0],
U=Umat,
Uprime=Upmat)
# Print some information on the master node
mpi.report('Greens function structure is: %s ' % gf_struct)
mpi.report('U Matrix set to:\n%s' % Umat)
mpi.report('Up Matrix set to:\n%s' % Upmat)
# Parameters for the CTQMC Solver
for e in eal:
assert is_diagonal_matrix(np.dot(np.dot(t_solver_eal[0][e], eal[e].conj().T),t_solver_eal[0][e].conj().T))
dm = SK.density_matrix(method='using_point_integration')
for dmi in dm:
for e in dmi:
assert is_diagonal_matrix(np.dot(np.dot(t_solver_dm[0][e], dmi[e].conj().T),t_solver_dm[0][e].conj().T))
# Test convert_operator
SK = SumkDFT(hdf_file = 'SrVO3.ref.h5', use_dft_blocks=True)
BS = SK.block_structure
from triqs.operators.util import h_int_slater, U_matrix, t2g_submatrix, transform_U_matrix
U3x3 = t2g_submatrix(U_matrix(2, U_int=2, J_hund=0.2, basis='spheric'))
BS.transformation = [{'up':np.eye(3), 'down': np.eye(3)}]
H0 = h_int_slater(spin_names=['up','down'], orb_names=range(3), U_matrix=U3x3, off_diag=False)
H1 = h_int_slater(spin_names=['up','down'], orb_names=range(3), U_matrix=U3x3, off_diag=True)
assert( H0 == BS.convert_operator(H1) )
# Trafo Matrix switching index 1 & 2
BS.transformation = [{'up':np.array([[1,0,0],[0,0,1],[0,1,0]]), 'down': np.array([[1,0,0],[0,0,1],[0,1,0]])}]
H2 = BS.convert_operator(h_int_slater(spin_names=['up','down'], orb_names=[0,2,1], U_matrix=U3x3, off_diag=True))
assert( H0 == H2 )
BS.transformation = [{'up':np.array([[1,0,0],[0,1/np.sqrt(2),1/np.sqrt(2)],[0,1/np.sqrt(2),-1/np.sqrt(2)]]), 'down': np.array([[1,0,0],[0,1/np.sqrt(2),1/np.sqrt(2)],[0,1/np.sqrt(2),-1/np.sqrt(2)]])}]
H3 = BS.convert_operator(h_int_slater(spin_names=['up','down'], orb_names=[0,1,2], U_matrix=U3x3, off_diag=True))
for op in H3:
for c_op in op[0]:
def dmft_cycle():
filename = 'nio'
Converter = VaspConverter(filename=filename)
Converter.convert_dft_input()
SK = SumkDFT(hdf_file = filename+'.h5', use_dft_blocks = False)
beta = 5.0
Sigma = SK.block_structure.create_gf(beta=beta)
SK.put_Sigma([Sigma])
G = SK.extract_G_loc()
SK.analyse_block_structure_from_gf(G, threshold = 1e-2)
for i_sh in range(len(SK.deg_shells)):
num_block_deg_orbs = len(SK.deg_shells[i_sh])
mpi.report('found {0:d} blocks of degenerate orbitals in shell {1:d}'.format(num_block_deg_orbs, i_sh))
for iblock in range(num_block_deg_orbs):
mpi.report('block {0:d} consists of orbitals:'.format(iblock))
for keys in list(SK.deg_shells[i_sh][iblock].keys()):
mpi.report(' '+keys)
# Setup CTQMC Solver
n_orb = SK.corr_shells[0]['dim']
spin_names = ['up','down']
orb_names = [i for i in range(0,n_orb)]
#gf_struct = set_operator_structure(spin_names, orb_names, orb_hyb)
gf_struct = SK.gf_struct_solver[0]
mpi.report('Sumk to Solver: %s'%SK.sumk_to_solver)
mpi.report('GF struct sumk: %s'%SK.gf_struct_sumk)
mpi.report('GF struct solver: %s'%SK.gf_struct_solver)
S = Solver(beta=beta, gf_struct=gf_struct)
# Construct the Hamiltonian and save it in Hamiltonian_store.txt
H = Operator()
U = 8.0
J = 1.0
U_sph = U_matrix(l=2, U_int=U, J_hund=J)
U_cubic = transform_U_matrix(U_sph, spherical_to_cubic(l=2, convention=''))
Umat, Upmat = reduce_4index_to_2index(U_cubic)
H = h_int_density(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U=Umat, Uprime=Upmat)
# Print some information on the master node
mpi.report('Greens function structure is: %s '%gf_struct)
mpi.report('U Matrix set to:\n%s'%Umat)
mpi.report('Up Matrix set to:\n%s'%Upmat)
# Parameters for the CTQMC Solver
p = {}
p["max_time"] = -1
p["random_name"] = ""
p["random_seed"] = 123 * mpi.rank + 567
p["length_cycle"] = 100
p["n_warmup_cycles"] = 2000
p["n_cycles"] = 20000
p["fit_max_moment"] = 4
p["fit_min_n"] = 30
p["fit_max_n"] = 50
p["perform_tail_fit"] = True
# Double Counting: 0 FLL, 1 Held, 2 AMF
DC_type = 0
DC_value = 59.0
# Prepare hdf file and and check for previous iterations
n_iterations = 1
iteration_offset = 0
if mpi.is_master_node():
ar = HDFArchive(filename+'.h5','a')
if not 'DMFT_results' in ar: ar.create_group('DMFT_results')
if not 'Iterations' in ar['DMFT_results']: ar['DMFT_results'].create_group('Iterations')
if not 'DMFT_input' in ar: ar.create_group('DMFT_input')
if not 'Iterations' in ar['DMFT_input']: ar['DMFT_input'].create_group('Iterations')
if not 'code_versions' in ar['DMFT_input']: ar['DMFT_input'].create_group('code_versio\
ns')
ar['DMFT_input']['code_versions']["triqs_version"] = triqs_version.version
ar['DMFT_input']['code_versions']["triqs_git"] = triqs_version.git_hash
ar['DMFT_input']['code_versions']["cthyb_version"] = cthyb_version.version
ar['DMFT_input']['code_versions']["cthyb_git"] = cthyb_version.triqs_cthyb_hash
ar['DMFT_input']['code_versions']["dft_tools_version"] = dft_tools_version.version
ar['DMFT_input']['code_versions']["dft_tools_git"] = dft_tools_version.triqs_dft_tools_hash
if 'iteration_count' in ar['DMFT_results']:
iteration_offset = ar['DMFT_results']['iteration_count']+1
S.Sigma_iw = ar['DMFT_results']['Iterations']['Sigma_it'+str(iteration_offset-1)]
SK.dc_imp = ar['DMFT_results']['Iterations']['dc_imp'+str(iteration_offset-1)]
SK.dc_energ = ar['DMFT_results']['Iterations']['dc_energ'+str(iteration_offset-1)]
SK.chemical_potential = ar['DMFT_results']['Iterations']['chemical_potential'+str(iteration_offset-1)].real
ar['DMFT_input']["dmft_script_it"+str(iteration_offset)] = open(sys.argv[0]).read()
iteration_offset = mpi.bcast(iteration_offset)
S.Sigma_iw = mpi.bcast(S.Sigma_iw)
SK.dc_imp = mpi.bcast(SK.dc_imp)
SK.dc_energ = mpi.bcast(SK.dc_energ)
SK.chemical_potential = mpi.bcast(SK.chemical_potential)
# Calc the first G0
SK.symm_deg_gf(S.Sigma_iw, ish=0)
SK.put_Sigma(Sigma_imp = [S.Sigma_iw])
SK.calc_mu(precision=0.01)
S.G_iw << SK.extract_G_loc()[0]
SK.symm_deg_gf(S.G_iw, ish=0)
#Init the DC term and the self-energy if no previous iteration was found
if iteration_offset == 0:
dm = S.G_iw.density()
SK.calc_dc(dm, U_interact=U, J_hund=J, orb=0, use_dc_formula=DC_type,use_dc_value=DC_value)
S.Sigma_iw << SK.dc_imp[0]['up'][0,0]
mpi.report('%s DMFT cycles requested. Starting with iteration %s.'%(n_iterations,iteration_offset))
# The infamous DMFT self consistency cycle
for it in range(iteration_offset, iteration_offset + n_iterations):
mpi.report('Doing iteration: %s'%it)
# Get G0
S.G0_iw << inverse(S.Sigma_iw + inverse(S.G_iw))
# Solve the impurity problem
S.solve(h_int = H, **p)
if mpi.is_master_node():
ar['DMFT_input']['Iterations']['solver_dict_it'+str(it)] = p
ar['DMFT_results']['Iterations']['Gimp_it'+str(it)] = S.G_iw
ar['DMFT_results']['Iterations']['Gtau_it'+str(it)] = S.G_tau
ar['DMFT_results']['Iterations']['Sigma_uns_it'+str(it)] = S.Sigma_iw
# Calculate double counting
dm = S.G_iw.density()
SK.calc_dc(dm, U_interact=U, J_hund=J, orb=0, use_dc_formula=DC_type,use_dc_value=DC_value)
# Get new G
SK.symm_deg_gf(S.Sigma_iw, ish=0)
SK.put_Sigma(Sigma_imp=[S.Sigma_iw])
SK.calc_mu(precision=0.01)
S.G_iw << SK.extract_G_loc()[0]
# print densities
for sig,gf in S.G_iw:
mpi.report("Orbital %s density: %.6f"%(sig,dm[sig][0,0]))
mpi.report('Total charge of Gloc : %.6f'%S.G_iw.total_density())
if mpi.is_master_node():
ar['DMFT_results']['iteration_count'] = it
ar['DMFT_results']['Iterations']['Sigma_it'+str(it)] = S.Sigma_iw
ar['DMFT_results']['Iterations']['Gloc_it'+str(it)] = S.G_iw
ar['DMFT_results']['Iterations']['G0loc_it'+str(it)] = S.G0_iw
ar['DMFT_results']['Iterations']['dc_imp'+str(it)] = SK.dc_imp
ar['DMFT_results']['Iterations']['dc_energ'+str(it)] = SK.dc_energ
ar['DMFT_results']['Iterations']['chemical_potential'+str(it)] = SK.chemical_potential
if mpi.is_master_node():
print('calculating mu...')
SK.chemical_potential = SK.calc_mu( precision = 0.000001 )
if mpi.is_master_node():
print('calculating GAMMA')
SK.calc_density_correction(dm_type='vasp')
if mpi.is_master_node():
print('calculating energy corrections')
correnerg = 0.5 * (S.G_iw * S.Sigma_iw).total_density()
dm = S.G_iw.density() # compute the density matrix of the impurity problem
SK.calc_dc(dm, U_interact=U, J_hund=J, orb=0, use_dc_formula=DC_type,use_dc_value=DC_value)
dc_energ = SK.dc_energ[0]
if mpi.is_master_node():
ar['DMFT_results']['Iterations']['corr_energy_it'+str(it)] = correnerg
ar['DMFT_results']['Iterations']['dc_energy_it'+str(it)] = dc_energ
if mpi.is_master_node(): del ar
return correnerg, dc_energ