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.
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# 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)