#!/usr/bin/env python
# Copyright 2014-2020 The PySCF Developers. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Authors: Zhi-Hao Cui <zhcui0408@gmail.com>
#
"""
Unrestricted DFT+U with kpoint sampling.
Based on KUHF routine.
Refs: PRB, 1998, 57, 1505.
"""
import numpy as np
from pyscf import lib
from pyscf.lib import logger
from pyscf import __config__
from pyscf.pbc.dft import kuks
from pyscf.pbc.dft.krkspu import set_U, make_minao_lo, mdot, KRKSpU
from pyscf.data.nist import HARTREE2EV
[docs]
def get_veff(ks, cell=None, dm=None, dm_last=0, vhf_last=0, hermi=1,
kpts=None, kpts_band=None):
"""
Coulomb + XC functional + (Hubbard - double counting) for KUKSpU.
"""
if cell is None: cell = ks.cell
if dm is None: dm = ks.make_rdm1()
if kpts is None: kpts = ks.kpts
# J + V_xc
vxc = kuks.get_veff(ks, cell, dm, dm_last=dm_last, vhf_last=vhf_last,
hermi=hermi, kpts=kpts, kpts_band=kpts_band)
vxc = _add_Vhubbard(vxc, ks, dm, kpts)
return vxc
def _add_Vhubbard(vxc, ks, dm, kpts):
'''Add Hubbard U to Vxc matrix inplace.
'''
C_ao_lo = ks.C_ao_lo
ovlp = ks.get_ovlp()
nkpts = len(kpts)
nlo = C_ao_lo.shape[-1]
rdm1_lo = np.zeros((2, nkpts, nlo, nlo), dtype=np.complex128)
for s in range(2):
for k in range(nkpts):
C_inv = np.dot(C_ao_lo[s, k].conj().T, ovlp[k])
rdm1_lo[s, k] = mdot(C_inv, dm[s][k], C_inv.conj().T)
is_ibz = hasattr(kpts, "kpts_ibz")
if is_ibz:
rdm1_lo_0 = kpts.dm_at_ref_cell(rdm1_lo)
alphas = ks.alpha
if not hasattr(alphas, '__len__'): # not a list or tuple
alphas = [alphas] * len(ks.U_idx)
E_U = 0.0
weight = getattr(kpts, "weights_ibz", np.repeat(1.0/nkpts, nkpts))
logger.info(ks, "-" * 79)
with np.printoptions(precision=5, suppress=True, linewidth=1000):
for idx, val, lab, alpha in zip(ks.U_idx, ks.U_val, ks.U_lab, alphas):
lab_string = " "
for l in lab:
lab_string += "%9s" %(l.split()[-1])
lab_sp = lab[0].split()
logger.info(ks, "local rdm1 of atom %s: ",
" ".join(lab_sp[:2]) + " " + lab_sp[2][:2])
U_mesh = np.ix_(idx, idx)
for s in range(2):
P_loc = 0.0
for k in range(nkpts):
S_k = ovlp[k]
C_k = C_ao_lo[s, k][:, idx]
P_k = rdm1_lo[s, k][U_mesh]
E_U += weight[k] * (val * 0.5) * (P_k.trace() - np.dot(P_k, P_k).trace())
vhub_loc = (np.eye(P_k.shape[-1]) - P_k * 2.0) * (val * 0.5)
if alpha is not None:
# The alpha perturbation is only applied to the linear term of
# the local density.
E_U += weight[k] * alpha * P_k.trace()
vhub_loc += np.eye(P_k.shape[-1]) * alpha
SC = np.dot(S_k, C_k)
vhub_loc = SC.dot(vhub_loc).dot(SC.conj().T)
if vxc.dtype == np.float64:
vhub_loc = vhub_loc.real
vxc[s,k] += vhub_loc
if not is_ibz:
P_loc += P_k
if is_ibz:
P_loc = rdm1_lo_0[s][U_mesh].real
else:
P_loc = P_loc.real / nkpts
logger.info(ks, "spin %s\n%s\n%s", s, lab_string, P_loc)
logger.info(ks, "-" * 79)
if E_U.real < 0.0 and all(np.asarray(ks.U_val) > 0):
logger.warn(ks, "E_U (%g) is negative...", E_U.real)
vxc = lib.tag_array(vxc, E_U=E_U.real)
return vxc
[docs]
def energy_elec(mf, dm_kpts=None, h1e_kpts=None, vhf=None):
"""
Electronic energy for KUKSpU.
"""
if h1e_kpts is None: h1e_kpts = mf.get_hcore(mf.cell, mf.kpts)
if dm_kpts is None: dm_kpts = mf.make_rdm1()
if vhf is None or getattr(vhf, 'ecoul', None) is None:
vhf = mf.get_veff(mf.cell, dm_kpts)
weight = getattr(mf.kpts, "weights_ibz",
np.array([1.0/len(h1e_kpts),]*len(h1e_kpts)))
e1 = (np.einsum('k,kij,kji', weight, h1e_kpts, dm_kpts[0]) +
np.einsum('k,kij,kji', weight, h1e_kpts, dm_kpts[1]))
tot_e = e1 + vhf.ecoul + vhf.exc + vhf.E_U
mf.scf_summary['e1'] = e1.real
mf.scf_summary['coul'] = vhf.ecoul.real
mf.scf_summary['exc'] = vhf.exc.real
mf.scf_summary['E_U'] = vhf.E_U.real
logger.debug(mf, 'E1 = %s Ecoul = %s Exc = %s EU = %s',
e1, vhf.ecoul, vhf.exc, vhf.E_U)
return tot_e.real, vhf.ecoul + vhf.exc + vhf.E_U
[docs]
class KUKSpU(kuks.KUKS):
"""
UKSpU class adapted for PBCs with k-point sampling.
"""
_keys = {"U_idx", "U_val", "C_ao_lo", "U_lab", 'alpha'}
get_veff = get_veff
energy_elec = energy_elec
to_hf = lib.invalid_method('to_hf')
def __init__(self, cell, kpts=np.zeros((1,3)), xc='LDA,VWN',
exxdiv=getattr(__config__, 'pbc_scf_SCF_exxdiv', 'ewald'),
U_idx=[], U_val=[], C_ao_lo='minao', minao_ref='MINAO', **kwargs):
"""
DFT+U args:
U_idx: can be
list of list: each sublist is a set of LO indices to add U.
list of string: each string is one kind of LO orbitals,
e.g. ['Ni 3d', '1 O 2pz'], in this case,
LO should be aranged as ao_labels order.
or a combination of these two.
U_val: a list of effective U [in eV], i.e. U-J in Dudarev's DFT+U.
each U corresponds to one kind of LO orbitals, should have
the same length as U_idx.
C_ao_lo: LO coefficients, can be
np.array, shape ((spin,), nkpts, nao, nlo),
string, in 'minao'.
minao_ref: reference for minao orbitals, default is 'MINAO'.
Attributes:
U_idx: same as the input.
U_val: effectiv U-J [in AU]
C_ao_loc: np.array
alpha: the perturbation [in AU] used to compute U in LR-cDFT.
Refs: Cococcioni and de Gironcoli, PRB 71, 035105 (2005)
"""
super(self.__class__, self).__init__(cell, kpts, xc=xc, exxdiv=exxdiv, **kwargs)
set_U(self, U_idx, U_val)
if isinstance(C_ao_lo, str):
if C_ao_lo.upper() == 'MINAO':
self.C_ao_lo = make_minao_lo(self, minao_ref)
else:
raise NotImplementedError
else:
self.C_ao_lo = np.asarray(C_ao_lo)
if self.C_ao_lo.ndim == 3:
self.C_ao_lo = np.asarray((self.C_ao_lo, self.C_ao_lo))
elif self.C_ao_lo.ndim == 4:
if self.C_ao_lo.shape[0] == 1:
self.C_ao_lo = np.asarray((self.C_ao_lo[0], self.C_ao_lo[0]))
assert self.C_ao_lo.shape[0] == 2
else:
raise ValueError
# The perturbation (eV) used to compute U in LR-cDFT.
self.alpha = None
[docs]
def dump_flags(self, verbose=None):
super().dump_flags(verbose)
log = logger.new_logger(self, verbose)
if log.verbose >= logger.INFO:
from pyscf.pbc.dft.krkspu import _print_U_info
_print_U_info(self, log)
return self
[docs]
def nuc_grad_method(self):
raise NotImplementedError
[docs]
def linear_response_u(mf_plus_u, alphalist=(0.02, 0.05, 0.08)):
'''
Refs:
[1] M. Cococcioni and S. de Gironcoli, Phys. Rev. B 71, 035105 (2005)
[2] H. J. Kulik, M. Cococcioni, D. A. Scherlis, and N. Marzari, Phys. Rev. Lett. 97, 103001 (2006)
[3] Heather J. Kulik, J. Chem. Phys. 142, 240901 (2015)
[4] https://hjkgrp.mit.edu/tutorials/2011-05-31-calculating-hubbard-u/
[5] https://hjkgrp.mit.edu/tutorials/2011-06-28-hubbard-u-multiple-sites/
Args:
alphalist :
alpha parameters (in eV) are the displacements for the linear
response calculations. For each alpha in this list, the DFT+U with
U=u0+alpha, U=u0-alpha are evaluated. u0 is the U value from the
reference mf_plus_u object, which will be treated as a standard DFT
functional.
'''
is_ibz = hasattr(mf_plus_u.kpts, "kpts_ibz")
if is_ibz:
raise NotImplementedError
assert isinstance(mf_plus_u, KUKSpU)
assert len(mf_plus_u.U_idx) > 0
if not mf_plus_u.converged:
mf_plus_u.run()
assert mf_plus_u.converged
# The bare density matrix without adding U
bare_dm = mf_plus_u.make_rdm1()
mf = mf_plus_u.copy()
log = logger.new_logger(mf)
alphalist = np.asarray(alphalist)
alphalist = np.append(-alphalist[::-1], alphalist)
nkpts = len(mf.kpts)
C_ao_lo = mf.C_ao_lo
ovlp = mf.get_ovlp()
C_inv = []
for local_idx in mf.U_idx:
C_inv.append(
[[C_ao_lo[0,k][:,local_idx].conj().T.dot(ovlp[k]) for k in range(nkpts)],
[C_ao_lo[1,k][:,local_idx].conj().T.dot(ovlp[k]) for k in range(nkpts)]])
bare_occupancies = []
final_occupancies = []
for alpha in alphalist:
mf.alpha = alpha / HARTREE2EV
mf.kernel(dm0=bare_dm)
local_occ = 0
for c in C_inv:
C_on_site = [[c[0][k].dot(mf.mo_coeff[0][k]) for k in range(nkpts)],
[c[1][k].dot(mf.mo_coeff[1][k]) for k in range(nkpts)]]
rdm1_lo = mf.make_rdm1(C_on_site, mf.mo_occ)
local_occ += sum(x.trace().real for x in rdm1_lo[0])
local_occ += sum(x.trace().real for x in rdm1_lo[1])
local_occ /= nkpts
final_occupancies.append(local_occ)
# The first iteration of SCF
fock = mf.get_fock(dm=bare_dm)
e, mo = mf.eig(fock, ovlp)
local_occ = 0
for c in C_inv:
C_on_site = [[c[0][k].dot(mo[0][k]) for k in range(nkpts)],
[c[1][k].dot(mo[1][k]) for k in range(nkpts)]]
rdm1_lo = mf.make_rdm1(C_on_site, mf.mo_occ)
local_occ += sum(x.trace().real for x in rdm1_lo[0])
local_occ += sum(x.trace().real for x in rdm1_lo[1])
local_occ /= nkpts
bare_occupancies.append(local_occ)
log.info('alpha=%f bare_occ=%g final_occ=%g',
alpha, bare_occupancies[-1], final_occupancies[-1])
chi0, occ0 = np.polyfit(alphalist, bare_occupancies, deg=1)
chif, occf = np.polyfit(alphalist, final_occupancies, deg=1)
log.info('Line fitting chi0 = %f x + %f', chi0, occ0)
log.info('Line fitting chif = %f x + %f', chif, occf)
Uresp = 1./chi0 - 1./chif
log.note('Uresp = %f, chi0 = %f, chif = %f', Uresp, chi0, chif)
return Uresp
