mirror of
https://github.com/triqs/dft_tools
synced 2024-12-22 20:34:38 +01:00
445 lines
19 KiB
Python
445 lines
19 KiB
Python
#from triqs_dft_tools.sumk_dft import *
|
|
from sumk_dft import *
|
|
#from triqs_dft_tools.converters.wien2k_converter import *
|
|
from converters.vasp_converter import *
|
|
#from pytriqs.applications.impurity_solvers.hubbard_I.hubbard_solver import Solver
|
|
from hf_solver import Solver
|
|
import shutil
|
|
|
|
class TestSumkDFT(SumkDFT):
|
|
# calculate and save occupancy matrix in the Bloch basis for VASP charge denity recalculation
|
|
def calc_density_correction(self, filename='GAMMA', dm_type='wien2k'):
|
|
r"""
|
|
Calculates the charge density correction and stores it into a file.
|
|
|
|
The charge density correction is needed for charge-self-consistent DFT+DMFT calculations.
|
|
It represents a density matrix of the interacting system defined in Bloch basis
|
|
and it is calculated from the sum over Matsubara frequecies of the full GF,
|
|
|
|
..math:: N_{\nu\nu'}(k) = \sum_{i\omega_{n}} G_{\nu\nu'}(k, i\omega_{n})
|
|
|
|
The density matrix for every `k`-point is stored into a file.
|
|
|
|
Parameters
|
|
----------
|
|
filename : string
|
|
Name of the file to store the charge density correction.
|
|
|
|
Returns
|
|
-------
|
|
(deltaN, dens) : tuple
|
|
Returns a tuple containing the density matrix `deltaN` and
|
|
the corresponing total charge `dens`.
|
|
|
|
"""
|
|
assert type(filename) == StringType, "calc_density_correction: filename has to be a string!"
|
|
assert dm_type in ('vasp', 'wien2k'), "'type' must be either 'vasp' or 'wienk'"
|
|
|
|
ntoi = self.spin_names_to_ind[self.SO]
|
|
spn = self.spin_block_names[self.SO]
|
|
dens = {sp: 0.0 for sp in spn}
|
|
|
|
# Fetch Fermi weights and energy window band indices
|
|
if dm_type == 'vasp':
|
|
fermi_weights = 0
|
|
band_window = 0
|
|
if mpi.is_master_node():
|
|
ar = HDFArchive(self.hdf_file,'r')
|
|
fermi_weights = ar['dft_misc_input']['dft_fermi_weights']
|
|
band_window = ar['dft_misc_input']['band_window']
|
|
del ar
|
|
fermi_weights = mpi.bcast(fermi_weights)
|
|
band_window = mpi.bcast(band_window)
|
|
|
|
# Convert Fermi weights to a density matrix
|
|
dens_mat_dft = {}
|
|
for sp in spn:
|
|
dens_mat_dft[sp] = [fermi_weights[ik, ntoi[sp], :].astype(numpy.complex_) for ik in xrange(self.n_k)]
|
|
|
|
# Set up deltaN:
|
|
deltaN = {}
|
|
for sp in spn:
|
|
deltaN[sp] = [numpy.zeros([self.n_orbitals[ik,ntoi[sp]],self.n_orbitals[ik,ntoi[sp]]], numpy.complex_) for ik in range(self.n_k)]
|
|
|
|
ikarray = numpy.array(range(self.n_k))
|
|
for ik in mpi.slice_array(ikarray):
|
|
G_latt_iw = self.lattice_gf(ik = ik, mu = self.chemical_potential, iw_or_w = "iw")
|
|
for bname,gf in G_latt_iw:
|
|
deltaN[bname][ik][:, :] = G_latt_iw[bname].density()
|
|
dens[bname] += self.bz_weights[ik] * G_latt_iw[bname].total_density()
|
|
if dm_type == 'vasp':
|
|
# In 'vasp'-mode subtract the DFT density matrix
|
|
diag_inds = numpy.diag_indices(self.n_orbitals[ik, ntoi[bname]])
|
|
deltaN[bname][ik][diag_inds] -= dens_mat_dft[bname][ik]
|
|
dens[bname] -= self.bz_weights[ik] * dens_mat_dft[bname][ik].sum().real
|
|
|
|
# mpi reduce:
|
|
for bname in deltaN:
|
|
for ik in range(self.n_k):
|
|
deltaN[bname][ik] = mpi.all_reduce(mpi.world, deltaN[bname][ik], lambda x,y : x+y)
|
|
dens[bname] = mpi.all_reduce(mpi.world, dens[bname], lambda x,y : x+y)
|
|
mpi.barrier()
|
|
|
|
# now save to file:
|
|
if dm_type == 'wien2k':
|
|
if mpi.is_master_node():
|
|
if self.SP == 0:
|
|
f = open(filename,'w')
|
|
else:
|
|
f = open(filename+'up','w')
|
|
f1 = open(filename+'dn','w')
|
|
# write chemical potential (in Rydberg):
|
|
f.write("%.14f\n"%(self.chemical_potential/self.energy_unit))
|
|
if self.SP != 0: f1.write("%.14f\n"%(self.chemical_potential/self.energy_unit))
|
|
# write beta in rydberg-1
|
|
f.write("%.14f\n"%(G_latt_iw.mesh.beta*self.energy_unit))
|
|
if self.SP != 0: f1.write("%.14f\n"%(G_latt_iw.mesh.beta*self.energy_unit))
|
|
|
|
if self.SP == 0: # no spin-polarization
|
|
|
|
for ik in range(self.n_k):
|
|
f.write("%s\n"%self.n_orbitals[ik,0])
|
|
for inu in range(self.n_orbitals[ik,0]):
|
|
for imu in range(self.n_orbitals[ik,0]):
|
|
valre = (deltaN['up'][ik][inu,imu].real + deltaN['down'][ik][inu,imu].real) / 2.0
|
|
valim = (deltaN['up'][ik][inu,imu].imag + deltaN['down'][ik][inu,imu].imag) / 2.0
|
|
f.write("%.14f %.14f "%(valre,valim))
|
|
f.write("\n")
|
|
f.write("\n")
|
|
f.close()
|
|
|
|
elif self.SP == 1: # with spin-polarization
|
|
|
|
# dict of filename: (spin index, block_name)
|
|
if self.SO == 0: to_write = {f: (0, 'up'), f1: (1, 'down')}
|
|
if self.SO == 1: to_write = {f: (0, 'ud'), f1: (0, 'ud')}
|
|
for fout in to_write.iterkeys():
|
|
isp, sp = to_write[fout]
|
|
for ik in range(self.n_k):
|
|
fout.write("%s\n"%self.n_orbitals[ik,isp])
|
|
for inu in range(self.n_orbitals[ik,isp]):
|
|
for imu in range(self.n_orbitals[ik,isp]):
|
|
fout.write("%.14f %.14f "%(deltaN[sp][ik][inu,imu].real,deltaN[sp][ik][inu,imu].imag))
|
|
fout.write("\n")
|
|
fout.write("\n")
|
|
fout.close()
|
|
elif dm_type == 'vasp':
|
|
# assert self.SP == 0, "Spin-polarized density matrix is not implemented"
|
|
|
|
if mpi.is_master_node():
|
|
with open(filename, 'w') as f:
|
|
f.write(" %i -1 ! Number of k-points, default number of bands\n"%(self.n_k))
|
|
for sp in spn:
|
|
for ik in xrange(self.n_k):
|
|
ib1 = band_window[0][ik, 0]
|
|
ib2 = band_window[0][ik, 1]
|
|
f.write(" %i %i %i\n"%(ik + 1, ib1, ib2))
|
|
for inu in xrange(self.n_orbitals[ik, 0]):
|
|
for imu in xrange(self.n_orbitals[ik, 0]):
|
|
if self.SP == 0:
|
|
valre = (deltaN['up'][ik][inu, imu].real + deltaN['down'][ik][inu, imu].real) / 2.0
|
|
valim = (deltaN['up'][ik][inu, imu].imag + deltaN['down'][ik][inu, imu].imag) / 2.0
|
|
else:
|
|
valre = deltaN[sp][ik][inu, imu].real
|
|
valim = deltaN[sp][ik][inu, imu].imag
|
|
|
|
f.write(" %.14f %.14f"%(valre, valim))
|
|
f.write("\n")
|
|
else:
|
|
raise NotImplementedError("Unknown density matrix type: '%s'"%(dm_type))
|
|
|
|
return deltaN, dens
|
|
|
|
def calc_hamiltonian_correction(self, filename='GAMMA'):
|
|
r"""
|
|
Calculates the charge density correction and stores it into a file.
|
|
|
|
The charge density correction is needed for charge-self-consistent DFT+DMFT calculations.
|
|
It represents a density matrix of the interacting system defined in Bloch basis
|
|
and it is calculated from the sum over Matsubara frequecies of the full GF,
|
|
|
|
..math:: N_{\nu\nu'}(k) = \sum_{i\omega_{n}} G_{\nu\nu'}(k, i\omega_{n})
|
|
|
|
The density matrix for every `k`-point is stored into a file.
|
|
|
|
Parameters
|
|
----------
|
|
filename : string
|
|
Name of the file to store the charge density correction.
|
|
|
|
Returns
|
|
-------
|
|
(deltaN, dens) : tuple
|
|
Returns a tuple containing the density matrix `deltaN` and
|
|
the corresponing total charge `dens`.
|
|
|
|
"""
|
|
assert type(filename) == StringType, "calc_density_correction: filename has to be a string!"
|
|
|
|
ntoi = self.spin_names_to_ind[self.SO]
|
|
spn = self.spin_block_names[self.SO]
|
|
dens = {sp: 0.0 for sp in spn}
|
|
|
|
# Fetch Fermi weights and energy window band indices
|
|
fermi_weights = 0
|
|
band_window = 0
|
|
if mpi.is_master_node():
|
|
ar = HDFArchive(self.hdf_file,'r')
|
|
fermi_weights = ar['dft_misc_input']['dft_fermi_weights']
|
|
band_window = ar['dft_misc_input']['band_window']
|
|
del ar
|
|
fermi_weights = mpi.bcast(fermi_weights)
|
|
band_window = mpi.bcast(band_window)
|
|
|
|
# Set up deltaH:
|
|
deltaH = {}
|
|
for sp in spn:
|
|
deltaH[sp] = [numpy.zeros([self.n_orbitals[ik,ntoi[sp]],self.n_orbitals[ik,ntoi[sp]]], numpy.complex_) for ik in range(self.n_k)]
|
|
|
|
ikarray = numpy.array(range(self.n_k))
|
|
for ik in mpi.slice_array(ikarray):
|
|
sigma_minus_dc = [s.copy() for s in self.Sigma_imp_iw]
|
|
sigma_minus_dc = self.add_dc('iw')
|
|
|
|
beta = self.Sigma_imp_iw[0].mesh.beta # override beta if Sigma_iw is present
|
|
n_iw = len(self.Sigma_imp_iw[0].mesh)
|
|
|
|
block_structure = [ range(self.n_orbitals[ik,ntoi[sp]]) for sp in spn ]
|
|
gf_struct = [ (spn[isp], block_structure[isp]) for isp in range(self.n_spin_blocks[self.SO]) ]
|
|
block_ind_list = [block for block,inner in gf_struct]
|
|
glist = lambda : [ GfImFreq(indices=inner,beta=beta,n_points=n_iw) for block,inner in gf_struct]
|
|
G_latt = BlockGf(name_list = block_ind_list, block_list = glist(), make_copies = False)
|
|
G_latt.zero()
|
|
|
|
for icrsh in range(self.n_corr_shells):
|
|
for bname, gf in G_latt:
|
|
gf += self.upfold(ik,icrsh,bname,sigma_minus_dc[icrsh][bname],gf)
|
|
|
|
for sp in spn:
|
|
deltaH[sp][ik][:, :] = G_latt[sp](0) # Any Matsubara frequency will do
|
|
# G_latt_iw = self.lattice_gf(ik = ik, mu = self.chemical_potential, iw_or_w = "iw")
|
|
# for bname,gf in G_latt_iw:
|
|
# deltaN[bname][ik][:, :] = G_latt_iw[bname].density()
|
|
# dens[bname] += self.bz_weights[ik] * G_latt_iw[bname].total_density()
|
|
# if dm_type == 'vasp':
|
|
## In 'vasp'-mode subtract the DFT density matrix
|
|
# diag_inds = numpy.diag_indices(self.n_orbitals[ik, ntoi[bname]])
|
|
# deltaN[bname][ik][diag_inds] -= dens_mat_dft[bname][ik]
|
|
# dens[bname] -= self.bz_weights[ik] * dens_mat_dft[bname][ik].sum().real
|
|
|
|
# mpi reduce:
|
|
for bname in deltaH:
|
|
for ik in range(self.n_k):
|
|
deltaH[bname][ik] = mpi.all_reduce(mpi.world, deltaH[bname][ik], lambda x,y : x+y)
|
|
mpi.barrier()
|
|
|
|
# now save to file:
|
|
if mpi.is_master_node():
|
|
with open(filename, 'w') as f:
|
|
f.write("H %i -1 ! Number of k-points, default number of bands\n"%(self.n_k))
|
|
for sp in spn:
|
|
for ik in xrange(self.n_k):
|
|
ib1 = band_window[0][ik, 0]
|
|
ib2 = band_window[0][ik, 1]
|
|
f.write(" %i %i %i\n"%(ik + 1, ib1, ib2))
|
|
for inu in xrange(self.n_orbitals[ik, 0]):
|
|
for imu in xrange(self.n_orbitals[ik, 0]):
|
|
if self.SP == 0:
|
|
valre = (deltaH['up'][ik][inu, imu].real + deltaH['down'][ik][inu, imu].real) / 2.0
|
|
valim = (deltaH['up'][ik][inu, imu].imag + deltaH['down'][ik][inu, imu].imag) / 2.0
|
|
else:
|
|
valre = deltaH[sp][ik][inu, imu].real
|
|
valim = deltaH[sp][ik][inu, imu].imag
|
|
|
|
f.write(" %.14f %.14f"%(valre, valim))
|
|
f.write("\n")
|
|
return deltaH
|
|
|
|
def dmft_cycle():
|
|
lda_filename = 'vasp'
|
|
beta = 400
|
|
U_int = 4.00
|
|
J_hund = 0.70
|
|
Loops = 1 # Number of DMFT sc-loops
|
|
Mix = 1.0 # Mixing factor in QMC
|
|
DC_type = 0 # 0...FLL, 1...Held, 2... AMF, 3...Lichtenstein
|
|
DC_Mix = 1.0 # 1.0 ... all from imp; 0.0 ... all from Gloc
|
|
useBlocs = False # use bloc structure from LDA input
|
|
useMatrix = True # use the U matrix calculated from Slater coefficients instead of (U+2J, U, U-J)
|
|
chemical_potential_init=-0.0 # initial chemical potential
|
|
|
|
use_dudarev = True
|
|
|
|
HDFfilename = lda_filename+'.h5'
|
|
|
|
# Convert DMFT input:
|
|
# Can be commented after the first run
|
|
Converter = VaspConverter(filename=lda_filename)
|
|
Converter.convert_dft_input()
|
|
|
|
#check if there are previous runs:
|
|
previous_runs = 0
|
|
previous_present = False
|
|
|
|
if mpi.is_master_node():
|
|
ar = HDFArchive(HDFfilename,'a')
|
|
if 'iterations' in ar:
|
|
previous_present = True
|
|
previous_runs = ar['iterations']
|
|
else:
|
|
previous_runs = 0
|
|
previous_present = False
|
|
del ar
|
|
|
|
mpi.barrier()
|
|
previous_runs = mpi.bcast(previous_runs)
|
|
previous_present = mpi.bcast(previous_present)
|
|
|
|
# Init the SumK class
|
|
#SK=SumkDFT(hdf_file=lda_filename+'.h5',use_dft_blocks=False)
|
|
SK=TestSumkDFT(hdf_file=lda_filename+'.h5',use_dft_blocks=False)
|
|
|
|
Norb = SK.corr_shells[0]['dim']
|
|
l = SK.corr_shells[0]['l']
|
|
|
|
# Init the Hubbard-I solver:
|
|
S = Solver(beta = beta, l = l, dudarev=use_dudarev)
|
|
|
|
# DEBUG
|
|
#SK.put_Sigma(Sigma_imp=[S.Sigma])
|
|
#dH = SK.calc_hamiltonian_correction(filename='GAMMA')
|
|
# END DEBUG
|
|
|
|
chemical_potential=chemical_potential_init
|
|
# load previous data: old self-energy, chemical potential, DC correction
|
|
if (previous_present):
|
|
mpi.report("Using stored data for initialisation")
|
|
if (mpi.is_master_node()):
|
|
ar = HDFArchive(HDFfilename,'a')
|
|
S.Sigma <<= ar['SigmaF']
|
|
del ar
|
|
things_to_load=['chemical_potential','dc_imp']
|
|
old_data=SK.load(things_to_load)
|
|
chemical_potential=old_data[0]
|
|
SK.dc_imp=old_data[1]
|
|
S.Sigma = mpi.bcast(S.Sigma)
|
|
chemical_potential=mpi.bcast(chemical_potential)
|
|
SK.dc_imp=mpi.bcast(SK.dc_imp)
|
|
|
|
|
|
# DMFT loop:
|
|
for Iteration_Number in range(1,Loops+1):
|
|
|
|
itn = Iteration_Number + previous_runs
|
|
|
|
# put Sigma into the SumK class:
|
|
S.Sigma.zero() # !!!!
|
|
SK.put_Sigma(Sigma_imp = [ S.Sigma ])
|
|
|
|
# Compute the SumK, possibly fixing mu by dichotomy
|
|
if SK.density_required and (Iteration_Number > 1):
|
|
chemical_potential = SK.calc_mu( precision = 0.001 )
|
|
else:
|
|
mpi.report("No adjustment of chemical potential\nTotal density = %.3f"%SK.total_density(mu=chemical_potential))
|
|
|
|
# Density:
|
|
S.G <<= SK.extract_G_loc()[0]
|
|
mpi.report("Total charge of Gloc : %.6f"%S.G.total_density())
|
|
|
|
# calculated DC at the first run to have reasonable initial non-interacting atomic level positions
|
|
if ((Iteration_Number==1)and(previous_present==False)):
|
|
if use_dudarev:
|
|
dc_value_init = 0.0
|
|
else:
|
|
dc_value_init=U_int/2.0
|
|
dm=S.G.density()
|
|
SK.calc_dc( dm, U_interact = U_int, J_hund = J_hund, orb = 0, use_dc_formula = DC_type, use_dc_value=dc_value_init)
|
|
|
|
# calculate non-interacting atomic level positions:
|
|
# eal = SK.eff_atomic_levels()[0]
|
|
# S.set_atomic_levels( eal = eal )
|
|
|
|
# solve it:
|
|
corr_energy, dft_dc = S.solve(U_int = U_int, J_hund = J_hund, verbosity = 1)
|
|
|
|
# Now mix Sigma and G:
|
|
if ((itn>1)or(previous_present)):
|
|
if (mpi.is_master_node()and (Mix<1.0)):
|
|
ar = HDFArchive(HDFfilename,'r')
|
|
mpi.report("Mixing Sigma and G with factor %s"%Mix)
|
|
if ('SigmaF' in ar):
|
|
S.Sigma <<= Mix * S.Sigma + (1.0-Mix) * ar['SigmaF']
|
|
if ('GF' in ar):
|
|
S.G <<= Mix * S.G + (1.0-Mix) * ar['GF']
|
|
del ar
|
|
S.G = mpi.bcast(S.G)
|
|
S.Sigma = mpi.bcast(S.Sigma)
|
|
|
|
# after the Solver has finished, set new double counting:
|
|
# dm = S.G.density()
|
|
# if not use_dudarev:
|
|
# SK.calc_dc( dm, U_interact = U_int, J_hund = J_hund, orb = 0, use_dc_formula = DC_type )
|
|
|
|
# correlation energy calculations:
|
|
correnerg = 0.5 * (S.G * S.Sigma).total_density()
|
|
mpi.report("Corr. energy = %s"%correnerg)
|
|
|
|
# store the impurity self-energy, GF as well as correlation energy in h5
|
|
if (mpi.is_master_node()):
|
|
ar = HDFArchive(HDFfilename,'a')
|
|
ar['iterations'] = itn
|
|
ar['chemical_cotential%s'%itn] = chemical_potential
|
|
ar['SigmaF'] = S.Sigma
|
|
ar['GF'] = S.G
|
|
ar['correnerg%s'%itn] = correnerg
|
|
ar['DCenerg%s'%itn] = SK.dc_energ
|
|
del ar
|
|
|
|
#Save essential SumkDFT data:
|
|
things_to_save=['chemical_potential','dc_energ','dc_imp']
|
|
SK.save(things_to_save)
|
|
|
|
# print out occupancy matrix of Ce 4f
|
|
# mpi.report("Orbital densities of impurity Green function:")
|
|
# for s in dm:
|
|
# mpi.report("Block %s: "%s)
|
|
# for ii in range(len(dm[s])):
|
|
# str = ''
|
|
# for jj in range(len(dm[s])):
|
|
# if (dm[s][ii,jj].real>0):
|
|
# str += " %.4f"%(dm[s][ii,jj].real)
|
|
# else:
|
|
# str += " %.4f"%(dm[s][ii,jj].real)
|
|
# mpi.report(str)
|
|
mpi.report("Total charge of impurity problem : %.6f"%S.G.total_density())
|
|
|
|
|
|
# find exact chemical potential
|
|
#if (SK.density_required):
|
|
# SK.chemical_potential = SK.calc_mu( precision = 0.000001 )
|
|
# SK.chemical_potential = SK.calc_mu( precision = 0.01 )
|
|
|
|
#dN, d = SK.calc_density_correction(filename='GAMMA', dm_type='vasp')
|
|
#mpi.report("Trace of Density Matrix: %s"%d)
|
|
mpi.report("Storing Hamiltonian correction GAMMA...")
|
|
SK.put_Sigma(Sigma_imp=[S.Sigma])
|
|
dH = SK.calc_hamiltonian_correction(filename='GAMMA')
|
|
# shutil.copyfile('GAMMA', 'it%i.GAMMA'%(itn))
|
|
|
|
# store correlation energy contribution to be read by Wien2ki and then included to DFT+DMFT total energy
|
|
if (mpi.is_master_node()):
|
|
ar = HDFArchive(HDFfilename)
|
|
itn = ar['iterations']
|
|
correnerg = ar['correnerg%s'%itn]
|
|
DCenerg = ar['DCenerg%s'%itn]
|
|
del ar
|
|
correnerg -= DCenerg[0]
|
|
f=open(lda_filename+'.qdmft','a')
|
|
f.write("%.16f\n"%correnerg)
|
|
f.close()
|
|
|
|
return corr_energy, dft_dc
|
|
|
|
if __name__ == '__main__':
|
|
dmft_cycle()
|