H 3.80373444 3.80373444 2.54626556
H 2.54626556 3.80373444 3.80373444
H 3.80373444 2.54626556 3.80373444
'''
cell.basis = 'sto-3g'
cell.a = np.eye(3) * 6.35
cell.gs = [15] * 3
cell.verbose = 5
cell.build()
nk = [1, 1, 1]
abs_kpts = cell.make_kpts(nk)
kmf = dft.KRKS(cell, abs_kpts).mix_density_fit()
kmf.xc = 'pbe'
ekpt = kmf.run()
pywannier90.save_kmf(kmf, 'chk_mf') # Save the wave function
# Run pyWannier90 and plot WFs using pyWannier90
num_wann = 4
keywords = \
'''
exclude_bands : 1,6-9
begin projections
C:sp3
end projections
'''
# To use the saved wave function, replace kmf with 'chk_mf'
w90 = pywannier90.W90(kmf, cell, nk, num_wann, other_keywords=keywords)
w90.kernel()
w90.plot_wf(grid=[25, 25, 25], supercell=[1, 1, 1])
def self_consistent(self, get_band=False):
'''
Do self-consistent pDMET
'''
tprint.print_msg(
"--------------------------------------------------------------------"
)
tprint.print_msg("- SELF-CONSISTENT DMET CALCULATION ... STARTING -")
tprint.print_msg(" Convergence criteria")
tprint.print_msg(" Threshold :", self.SC_threshold)
tprint.print_msg(" Fitting 1-RDM of :", self.SC_CFtype)
if self.dft_CF is True:
tprint.print_msg(" DF-like cost function:", self.xc)
if self.damping != 1.0:
tprint.print_msg(" Damping factor :", self.damping)
if self.DIIS:
tprint.print_msg(
" DIIS start at %dth cycle and using %d previous umats" %
(self.DIIS_m, self.DIIS_n))
#------------------------------------#
#---- SELF-CONSISTENT PROCEDURE ----#
#------------------------------------#
rdm1_kpts, rdm1_R0 = self.local.make_loc_1RDM(self.umat, self.OEH_type)
for cycle in range(self.SC_maxcycle):
tprint.print_msg("- CYCLE %d:" % (cycle + 1))
umat_old = self.umat
rdm1_R0_old = rdm1_R0
# Do one-shot with each uvec
self.one_shot(umat=self.umat)
tprint.print_msg(" + Chemical potential : %12.8f" %
(self.chempot))
# Optimize uvec to minimize the cost function
if self.dft_CF == True:
result = optimize.minimize(self.CF,
self.uvec,
method='L-BFGS-B',
jac=None,
options={
'disp': False,
'gtol': 1e-6
},
bounds=self.bounds)
else:
result = optimize.minimize(self.CF,
self.uvec,
method=self.SC_method,
jac=self.CF_grad,
options={
'disp': False,
'gtol': 1e-12
})
# result = optimize.minimize(self.CF, self.uvec, method = self.SC_method , options = {'disp': False, 'gtol': 1e-12})
if result.success == False:
tprint.print_msg(
" WARNING: Correlation potential is not converged")
uvec = result.x
self.umat = self.uvec2umat(uvec)
#rdm1_kpts, rdm1_R0 = self.local.make_loc_1RDM(self.umat, self.OEH_type)
# Construct new global 1RDM in k-space
global_corr_1RDM = self.local.get_1RDM_Rs(self.loc_corr_1RDM_R0)
global_corr_1RDM = 0.5 * (global_corr_1RDM.T.conj() +
global_corr_1RDM)
loc_1RDM_R0 = global_corr_1RDM[:, :self.Nimp].reshape(
self.Nkpts, self.Nimp, self.Nimp)
rdm1_R0 = loc_1RDM_R0
# Remove arbitrary chemical potential shifts
if self.dft_CF == False:
self.umat = self.umat - np.eye(
self.umat.shape[0]) * np.average(np.diag(self.umat))
if self.verbose > 0:
tprint.print_msg(" + Correlation potential vector : ",
uvec)
umat_diff = umat_old - self.umat
rdm_diff = rdm1_R0_old - rdm1_R0
norm_u = np.linalg.norm(umat_diff)
norm_rdm = np.linalg.norm(rdm_diff)
tprint.print_msg(" + Cost function : %20.15f" %
(result.fun))
tprint.print_msg(" + 2-norm of umat difference : %20.15f" %
(norm_u))
tprint.print_msg(" + 2-norm of rdm1 difference : %20.15f" %
(norm_rdm))
# Export band structure at every cycle:
if get_band:
band = self.get_bands()
print("BAND", band.mo_energy_kpts[0])
print(
"Gap", 27.211 *
(band.mo_energy_kpts[0][4] - band.mo_energy_kpts[0][3]))
pywannier90.save_kmf(
band,
str(self.solver) + '_band_cyc_' + str(cycle + 1))
# Check convergence of 1-RDM
if self.dft_CF == True:
if (norm_rdm <= self.SC_threshold): break
elif (norm_u <= self.SC_threshold):
break
if self.DIIS == True:
self.umat = self._diis.update(cycle, self.umat, umat_diff)
if self.damping != 1.0:
self.umat = (
1.0 - self.damping) * umat_old + self.damping * self.umat
self.uvec = self.umat2uvec(self.umat)
tprint.print_msg()
tprint.print_msg("- SELF-CONSISTENT DMET CALCULATION ... DONE -")
tprint.print_msg(
"--------------------------------------------------------------------"
)
def projected_DMET(self, get_band=False):
'''
Do projected DMET
'''
tprint.print_msg(
"--------------------------------------------------------------------"
)
tprint.print_msg("- p-DMET CALCULATION ... STARTING -")
tprint.print_msg(" Convergence criteria")
tprint.print_msg(" Threshold :", self.SC_threshold)
tprint.print_msg(" Fitting 1-RDM of :", self.SC_CFtype)
if self.damping != 1.0:
tprint.print_msg(" Damping factor :", self.damping)
if self.DIIS:
tprint.print_msg(
" DIIS start at %dth cycle and using %d previous umats" %
(self.DIIS_m, self.DIIS_n))
#------------------------------------#
#---- SELF-CONSISTENT PROCEDURE ----#
#------------------------------------#
self.loc_1RDM_kpts, self.loc_1RDM_R0 = self.local.make_loc_1RDM(
0.0, self.OEH_type)
global_corr_1RDM = self.local.k_to_R(self.loc_1RDM_kpts)
for cycle in range(self.SC_maxcycle):
tprint.print_msg("- CYCLE %d:" % (cycle + 1))
global_corr_1RDM_old = global_corr_1RDM
self.one_shot(proj_DMET=True)
if self._is_gamma == False:
tprint.print_msg(" + Chemical potential : %12.8f" %
(self.chempot))
# Construct new global 1RDM in k-space
global_corr_1RDM = self.local.get_1RDM_Rs(self.loc_corr_1RDM_R0)
global_corr_1RDM = 0.5 * (global_corr_1RDM.T.conj() +
global_corr_1RDM)
global_corr_1RDM_residual = global_corr_1RDM_old - global_corr_1RDM
norm_1RDM = np.linalg.norm(
global_corr_1RDM_residual) / self.kpts.shape[0]
tprint.print_msg(" + 2-norm of rdm1 difference : %20.15f" %
(norm_1RDM))
if get_band == True:
band = self.get_bands()
pywannier90.save_kmf(
band,
str(self.solver) + '_band_cyc_' + str(cycle + 1))
# Check convergence of 1-RDM
if norm_1RDM <= self.SC_threshold:
break
if self.DIIS == True:
global_corr_1RDM = self._diis.update(
cycle, global_corr_1RDM, global_corr_1RDM_residual)
if self.damping != 1.0:
global_corr_1RDM = self.damping * global_corr_1RDM + (
1 - self.damping) * global_corr_1RDM_old
# Construct new mean-field 1-RDM from the correlated one
eigenvals, eigenvecs = np.linalg.eigh(global_corr_1RDM)
idx = (-eigenvals).argsort()
eigenvals = eigenvals[idx]
eigenvecs = eigenvecs[:, idx]
num_pairs = self.Nelec_total // 2
global_mf_1RDM = 2 * eigenvecs[:, :num_pairs].dot(
eigenvecs[:, :num_pairs].T)
if self._is_gamma == True:
self.loc_1RDM_R0 = global_mf_1RDM.reshape(
1, self.Norbs, self.Norbs) + self.loc_core_1RDM
else:
self.loc_1RDM_R0 = global_mf_1RDM[:, :self.Nimp].reshape(
self.Nkpts, self.Nimp, self.Nimp)
self.loc_1RDM_kpts = self.local.R0_to_k(self.loc_1RDM_R0)
tprint.print_msg()
tprint.print_msg("- p-DMET CALCULATION ... DONE -")
tprint.print_msg(
"--------------------------------------------------------------------"
)
H 2.54626556 3.80373444 3.80373444
H 3.80373444 2.54626556 3.80373444
'''
cell.basis = 'sto-3g'
cell.a = np.eye(3) * 6.35
cell.gs = [15] * 3
cell.verbose = 5
cell.build()
nk = [1, 1, 1]
abs_kpts = cell.make_kpts(nk)
kmf = dft.KRKS(cell, abs_kpts).mix_density_fit()
kmf.xc = 'pbe'
ekpt = kmf.run()
pywannier90.save_kmf(kmf, 'chk_mf') # Save the wave function
# Run pyWannier90 and plot WFs using pyWannier90
num_wann = 4
keywords = \
'''
exclude_bands : 1,6-9
begin projections
C:sp3
end projections
'''
# To use the saved wave function, replace kmf with 'chk_mf'
w90 = pywannier90.W90(kmf, cell, nk, num_wann, other_keywords = keywords)
w90.kernel()
w90.plot_wf(grid=[25,25,25], supercell = [1,1,1])