mirror of
https://github.com/triqs/dft_tools
synced 2024-11-18 12:03:50 +01:00
202 lines
8.0 KiB
Python
202 lines
8.0 KiB
Python
from itertools import *
|
|
import numpy as np
|
|
import pytriqs.utility.mpi as mpi
|
|
from pytriqs.archive import *
|
|
from pytriqs.gf import *
|
|
import sys, pytriqs.version as triqs_version
|
|
from triqs_dft_tools.sumk_dft import *
|
|
from triqs_dft_tools.sumk_dft_tools import *
|
|
from pytriqs.operators.util.hamiltonians import *
|
|
from pytriqs.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_converter import *
|
|
|
|
|
|
import warnings
|
|
warnings.filterwarnings("ignore", category=FutureWarning)
|
|
|
|
|
|
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 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.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.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,orb=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, orb=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,orb=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
|