nio_csc.py
Download nio_csc.py.
from itertools import *
import numpy as np
import triqs.utility.mpi as mpi
from h5 import *
from triqs.gf import *
import sys, triqs.version as triqs_version
from triqs_dft_tools.sumk_dft import *
from triqs_dft_tools.sumk_dft_tools import *
from triqs.operators.util.hamiltonians import *
from triqs.operators.util.U_matrix import *
from triqs_cthyb import *
import triqs_cthyb.version as cthyb_version
import triqs_dft_tools.version as dft_tools_version
from triqs_dft_tools.converters.vasp import *
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
def dmft_cycle():
filename = 'vasp'
Converter = VaspConverter(filename=filename, proj_or_hk='hk')
Converter.convert_dft_input()
beta = 5.0
mesh = MeshImFreq(beta=beta, S='Fermion', n_iw=1000)
SK = SumkDFT(hdf_file=filename+'.h5', use_dft_blocks=False, mesh=mesh)
Sigma = SK.block_structure.create_gf(mesh=mesh)
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']
gf_struct = SK.gf_struct_solver_list[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, n_iw=1000)
# Construct the Hamiltonian and save it in Hamiltonian_store.txt
H = Operator()
U = 8.0
J = 1.0
U_sph = U_matrix_slater(l=2, U_int=U, J_hund=J)
U_cubic = transform_U_matrix(U_sph, spherical_to_cubic(l=2, convention='vasp'))
Umat, Upmat = reduce_4index_to_2index(U_cubic)
H = h_int_density(spin_names, n_orb,
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_versions')
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
ar['DMFT_input']['sumk_block_structure'] = SK.block_structure
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=0.001)
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].real))
mpi.report('Total charge of Gloc : %.6f' % S.G_iw.total_density().real)
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)
if mpi.is_master_node():
ar['DMFT_results']['Iterations']['corr_energy_it'+str(it)] = correnerg
ar['DMFT_results']['Iterations']['dc_energy_it'+str(it)] = SK.dc_energ[0]
if mpi.is_master_node():
del ar
return correnerg, SK