Source code for pyscf.dft.gks_symm
#!/usr/bin/env python
# Copyright 2014-2022 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.
#
# Author: Qiming Sun <osirpt.sun@gmail.com>
#
'''
Generalized Kohn-Sham
'''
from pyscf import lib
from pyscf.lib import logger
from pyscf.scf import ghf_symm
from pyscf.dft import gks
from pyscf.dft import rks
from pyscf.dft.numint2c import NumInt2C
[docs]
class GKS(rks.KohnShamDFT, ghf_symm.GHF):
''' Restricted Kohn-Sham '''
def __init__(self, mol, xc='LDA,VWN'):
ghf_symm.GHF.__init__(self, mol)
rks.KohnShamDFT.__init__(self, xc)
self._numint = NumInt2C()
[docs]
def dump_flags(self, verbose=None):
ghf_symm.GHF.dump_flags(self, verbose)
rks.KohnShamDFT.dump_flags(self, verbose)
logger.info(self, 'collinear = %s', self._numint.collinear)
if self._numint.collinear[0] == 'm':
logger.info(self, 'mcfun spin_samples = %s', self._numint.spin_samples)
logger.info(self, 'mcfun collinear_thrd = %s', self._numint.collinear_thrd)
logger.info(self, 'mcfun collinear_samples = %s', self._numint.collinear_samples)
return self
get_veff = gks.get_veff
energy_elec = rks.energy_elec
@property
def collinear(self):
return self._numint.collinear
@collinear.setter
def collinear(self, val):
self._numint.collinear = val
[docs]
def nuc_grad_method(self):
raise NotImplementedError
to_gpu = lib.to_gpu
if __name__ == '__main__':
import numpy
from pyscf import gto
mol = gto.Mole()
mol.verbose = 3
mol.atom = 'H 0 0 0; H 0 0 1; O .5 .6 .2'
mol.symmetry = True
mol.basis = 'ccpvdz'
mol.build()
mf = GKS(mol)
mf.xc = 'b3lyp'
mf.kernel()
dm = mf.init_guess_by_1e(mol)
dm = dm + 0j
nao = mol.nao_nr()
numpy.random.seed(12)
dm[:nao,nao:] = numpy.random.random((nao,nao)) * .1j
dm[nao:,:nao] = dm[:nao,nao:].T.conj()
mf.kernel(dm)
mf.canonicalize(mf.mo_coeff, mf.mo_occ)
mf.analyze()
print(mf.spin_square())
print(mf.e_tot - -76.2760114849027)