#!/usr/bin/env python
# Copyright 2014-2019 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>
#
import ctypes
import numpy
import scipy.linalg
from pyscf import lib
from pyscf import scf
from pyscf.lib import logger
from pyscf.ao2mo import _ao2mo
DEBUG = False
libri = lib.load_library('libri')
[docs]
def density_fit(mf, auxbasis=None, with_df=None, only_dfj=False):
'''For the given SCF object, update the J, K matrix constructor with
corresponding density fitting integrals.
Args:
mf : an SCF object
Kwargs:
auxbasis : str or basis dict
Same format as the input attribute mol.basis. If auxbasis is set to
None, an optimal auxiliary basis set will be selected based on the
AO basis set, according to the records in the basis set exchange
database and the predefined mappings in `pyscf.df.addons.DEFAULT_AUXBASIS`.
For DFT methods, the selection of auxiliary basis sets is also
influenced by the xc functional. The J-FIT basis set will be used for
local and semi-local functionals (LDA, GGA, and meta-GGA). JK-FIT
basis set will be employed for hybrid and RSH functionals.
If optimal auxiliary basis sets are not available, the PySCF built-in
even-tempered Gaussian basis set will be used.
only_dfj : str
Compute Coulomb integrals only and no approximation for HF
exchange. Same to RIJONX in ORCA
Returns:
An SCF object with a modified J, K matrix constructor which uses density
fitting integrals to compute J and K
Examples:
>>> mol = gto.M(atom='H 0 0 0; F 0 0 1', basis='ccpvdz', verbose=0)
>>> mf = scf.density_fit(scf.RHF(mol))
>>> mf.scf()
-100.005306000435510
>>> mol.symmetry = 1
>>> mol.build(0, 0)
>>> mf = scf.density_fit(scf.UHF(mol))
>>> mf.scf()
-100.005306000435510
'''
from pyscf import df
from pyscf.scf import dhf
from pyscf.df.addons import predefined_auxbasis
assert (isinstance(mf, scf.hf.SCF))
if with_df is None:
mol = mf.mol
if auxbasis is None and isinstance(mol.basis, str):
if isinstance(mf, scf.hf.KohnShamDFT):
xc = mf.xc
else:
xc = 'HF'
if xc == 'LDA,VWN':
# This is likely the default xc setting of a KS instance.
# Postpone the auxbasis assignment to with_df.build().
auxbasis = None
else:
auxbasis = predefined_auxbasis(mol, mol.basis, xc)
if isinstance(mf, dhf.UHF):
with_df = df.DF4C(mol, auxbasis)
else:
with_df = df.DF(mol, auxbasis)
with_df.max_memory = mf.max_memory
with_df.stdout = mf.stdout
with_df.verbose = mf.verbose
if isinstance(mf, _DFHF):
mf = mf.copy()
mf.with_df = with_df
mf.only_dfj = only_dfj
return mf
dfmf = _DFHF(mf, with_df, only_dfj)
return lib.set_class(dfmf, (_DFHF, mf.__class__))
# 1. A tag to label the derived SCF class
# 2. A hook to register DF specific methods, such as nuc_grad_method.
class _DFHF:
'''
Density fitting SCF class
Attributes for density-fitting SCF:
auxbasis : str or basis dict
Same format to the input attribute mol.basis.
The default basis 'weigend+etb' means weigend-coulomb-fit basis
for light elements and even-tempered basis for heavy elements.
with_df : DF object
Set mf.with_df = None to switch off density fitting mode.
See also the documents of class for other SCF attributes.
'''
__name_mixin__ = 'DF'
_keys = {'with_df', 'only_dfj'}
def __init__(self, mf, df=None, only_dfj=None):
self.__dict__.update(mf.__dict__)
self._eri = None
self.with_df = df
self.only_dfj = only_dfj
# Unless DF is used only for J matrix, disable direct_scf for K build.
# It is more efficient to construct K matrix with MO coefficients than
# the incremental method in direct_scf.
self.direct_scf = only_dfj
def undo_df(self):
'''Remove the DFHF Mixin'''
obj = lib.view(self, lib.drop_class(self.__class__, _DFHF))
del obj.with_df, obj.only_dfj
return obj
def reset(self, mol=None):
self.with_df.reset(mol)
return super().reset(mol)
def get_jk(self, mol=None, dm=None, hermi=1, with_j=True, with_k=True,
omega=None):
assert (with_j or with_k)
if dm is None: dm = self.make_rdm1()
if not self.with_df:
return super().get_jk(mol, dm, hermi, with_j, with_k, omega)
vj = vk = None
with_dfk = with_k and not self.only_dfj
if with_j or with_dfk:
if isinstance(self, scf.ghf.GHF):
def jkbuild(mol, dm, hermi, with_j, with_k, omega=None):
vj, vk = self.with_df.get_jk(dm.real, hermi, with_j, with_k,
self.direct_scf_tol, omega)
if dm.dtype == numpy.complex128:
vjI, vkI = self.with_df.get_jk(dm.imag, hermi, with_j, with_k,
self.direct_scf_tol, omega)
if with_j:
vj = vj + vjI * 1j
if with_k:
vk = vk + vkI * 1j
return vj, vk
vj, vk = scf.ghf.get_jk(mol, dm, hermi, with_j, with_dfk,
jkbuild, omega)
else:
vj, vk = self.with_df.get_jk(dm, hermi, with_j, with_dfk,
self.direct_scf_tol, omega)
if with_k and not with_dfk:
vk = super().get_jk(mol, dm, hermi, False, True, omega)[1]
return vj, vk
# for pyscf 1.0, 1.1 compatibility
@property
def _cderi(self):
naux = self.with_df.get_naoaux()
return next(self.with_df.loop(blksize=naux))
@_cderi.setter
def _cderi(self, x):
self.with_df._cderi = x
@property
def auxbasis(self):
return getattr(self.with_df, 'auxbasis', None)
def nuc_grad_method(self):
from pyscf.df.grad import rhf, rohf, uhf, rks, roks, uks
if self.istype('_Solvation'):
raise NotImplementedError(
'Gradients of solvent are not computed. '
'Solvent must be applied after density fitting method, e.g.\n'
'mf = mol.RKS().density_fit().PCM()'
)
if isinstance(self, scf.uhf.UHF):
if isinstance(self, scf.hf.KohnShamDFT):
return uks.Gradients(self)
else:
return uhf.Gradients(self)
elif isinstance(self, scf.rohf.ROHF):
if isinstance(self, scf.hf.KohnShamDFT):
return roks.Gradients(self)
else:
return rohf.Gradients(self)
elif isinstance(self, scf.rhf.RHF):
if isinstance(self, scf.hf.KohnShamDFT):
return rks.Gradients(self)
else:
return rhf.Gradients(self)
else:
raise NotImplementedError
Gradients = nuc_grad_method
def Hessian(self):
from pyscf.df.hessian import rhf, uhf, rks, uks
if self.istype('_Solvation'):
raise NotImplementedError(
'Hessian of solvent are not computed. '
'Solvent must be applied after density fitting method, e.g.\n'
'mf = mol.RKS().density_fit().PCM()'
)
if isinstance(self, (scf.uhf.UHF, scf.rohf.ROHF)):
if isinstance(self, scf.hf.KohnShamDFT):
return uks.Hessian(self)
else:
return uhf.Hessian(self)
elif isinstance(self, scf.rohf.ROHF):
raise NotImplementedError
elif isinstance(self, scf.rhf.RHF):
if isinstance(self, scf.hf.KohnShamDFT):
return rks.Hessian(self)
else:
return rhf.Hessian(self)
else:
raise NotImplementedError
def method_not_implemented(self, *args, **kwargs):
raise NotImplementedError
NMR = method_not_implemented
NSR = method_not_implemented
Polarizability = method_not_implemented
RotationalGTensor = method_not_implemented
def MP2(self, frozen=None, auxbasis=None):
mp_obj = self.DFMP2()
if auxbasis is not None:
mp_obj.with_df.auxbasis = auxbasis
return mp_obj
CISD = method_not_implemented
def CCSD(self, frozen=None, auxbasis=None):
from pyscf.cc import dfccsd, dfuccsd
cc_obj = self.DFCCSD(frozen)
if auxbasis is not None:
cc_obj.with_df.auxbasis = auxbasis
return cc_obj
def CASCI(self, ncas, nelecas, auxbasis=None, ncore=None):
from pyscf import mcscf
return mcscf.DFCASCI(self, ncas, nelecas, auxbasis, ncore)
def CASSCF(self, ncas, nelecas, auxbasis=None, ncore=None, frozen=None):
from pyscf import mcscf
return mcscf.DFCASSCF(self, ncas, nelecas, auxbasis, ncore, frozen)
def to_gpu(self):
obj = self.undo_df().to_gpu().density_fit()
return lib.to_gpu(self, obj)
[docs]
def get_jk(dfobj, dm, hermi=0, with_j=True, with_k=True, direct_scf_tol=1e-13):
assert (with_j or with_k)
if (not with_k and not dfobj.mol.incore_anyway and
# 3-center integral tensor is not initialized
dfobj._cderi is None):
return get_j(dfobj, dm, hermi, direct_scf_tol), None
t0 = t1 = (logger.process_clock(), logger.perf_counter())
log = logger.Logger(dfobj.stdout, dfobj.verbose)
fmmm = _ao2mo.libao2mo.AO2MOmmm_bra_nr_s2
fdrv = _ao2mo.libao2mo.AO2MOnr_e2_drv
ftrans = _ao2mo.libao2mo.AO2MOtranse2_nr_s2
null = lib.c_null_ptr()
dms = numpy.asarray(dm)
dm_shape = dms.shape
nao = dm_shape[-1]
dms = dms.reshape(-1,nao,nao)
nset = dms.shape[0]
vj = 0
vk = numpy.zeros_like(dms)
if numpy.iscomplexobj(dms):
if with_j:
vj = numpy.zeros_like(dms)
max_memory = dfobj.max_memory - lib.current_memory()[0]
blksize = max(4, int(min(dfobj.blockdim, max_memory*.22e6/8/nao**2)))
buf = numpy.empty((blksize,nao,nao))
buf1 = numpy.empty((nao,blksize,nao))
for eri1 in dfobj.loop(blksize):
naux, nao_pair = eri1.shape
eri1 = lib.unpack_tril(eri1, out=buf)
if with_j:
tmp = numpy.einsum('pij,nji->pn', eri1, dms.real)
vj.real += numpy.einsum('pn,pij->nij', tmp, eri1)
tmp = numpy.einsum('pij,nji->pn', eri1, dms.imag)
vj.imag += numpy.einsum('pn,pij->nij', tmp, eri1)
buf2 = numpy.ndarray((nao,naux,nao), buffer=buf1)
for k in range(nset):
buf2[:] = lib.einsum('pij,jk->ipk', eri1, dms[k].real)
vk[k].real += lib.einsum('ipk,pkj->ij', buf2, eri1)
buf2[:] = lib.einsum('pij,jk->ipk', eri1, dms[k].imag)
vk[k].imag += lib.einsum('ipk,pkj->ij', buf2, eri1)
t1 = log.timer_debug1('jk', *t1)
if with_j: vj = vj.reshape(dm_shape)
if with_k: vk = vk.reshape(dm_shape)
logger.timer(dfobj, 'df vj and vk', *t0)
return vj, vk
if with_j:
idx = numpy.arange(nao)
dmtril = lib.pack_tril(dms + dms.conj().transpose(0,2,1))
dmtril[:,idx*(idx+1)//2+idx] *= .5
if not with_k:
for eri1 in dfobj.loop():
# uses numpy.matmul
vj += dmtril.dot(eri1.T).dot(eri1)
elif getattr(dm, 'mo_coeff', None) is not None:
#TODO: test whether dm.mo_coeff matching dm
mo_coeff = numpy.asarray(dm.mo_coeff, order='F')
mo_occ = numpy.asarray(dm.mo_occ)
nmo = mo_occ.shape[-1]
mo_coeff = mo_coeff.reshape(-1,nao,nmo)
mo_occ = mo_occ.reshape(-1,nmo)
if mo_occ.shape[0] * 2 == nset: # handle ROHF DM
mo_coeff = numpy.vstack((mo_coeff, mo_coeff))
mo_occa = numpy.array(mo_occ> 0, dtype=numpy.double)
mo_occb = numpy.array(mo_occ==2, dtype=numpy.double)
assert (mo_occa.sum() + mo_occb.sum() == mo_occ.sum())
mo_occ = numpy.vstack((mo_occa, mo_occb))
orbo = []
for k in range(nset):
c = numpy.einsum('pi,i->pi', mo_coeff[k][:,mo_occ[k]>0],
numpy.sqrt(mo_occ[k][mo_occ[k]>0]))
orbo.append(numpy.asarray(c, order='F'))
max_memory = dfobj.max_memory - lib.current_memory()[0]
blksize = max(4, int(min(dfobj.blockdim, max_memory*.3e6/8/nao**2)))
buf = numpy.empty((blksize*nao,nao))
for eri1 in dfobj.loop(blksize):
naux, nao_pair = eri1.shape
assert (nao_pair == nao*(nao+1)//2)
if with_j:
# uses numpy.matmul
vj += dmtril.dot(eri1.T).dot(eri1)
for k in range(nset):
nocc = orbo[k].shape[1]
if nocc > 0:
buf1 = buf[:naux*nocc]
fdrv(ftrans, fmmm,
buf1.ctypes.data_as(ctypes.c_void_p),
eri1.ctypes.data_as(ctypes.c_void_p),
orbo[k].ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(naux), ctypes.c_int(nao),
(ctypes.c_int*4)(0, nocc, 0, nao),
null, ctypes.c_int(0))
vk[k] += lib.dot(buf1.T, buf1)
t1 = log.timer_debug1('jk', *t1)
else:
#:vk = numpy.einsum('pij,jk->pki', cderi, dm)
#:vk = numpy.einsum('pki,pkj->ij', cderi, vk)
rargs = (ctypes.c_int(nao), (ctypes.c_int*4)(0, nao, 0, nao),
null, ctypes.c_int(0))
dms = [numpy.asarray(x, order='F') for x in dms]
max_memory = dfobj.max_memory - lib.current_memory()[0]
blksize = max(4, int(min(dfobj.blockdim, max_memory*.22e6/8/nao**2)))
buf = numpy.empty((2,blksize,nao,nao))
for eri1 in dfobj.loop(blksize):
naux, nao_pair = eri1.shape
assert (nao_pair == nao*(nao+1)//2)
if with_j:
# uses numpy.matmul
vj += dmtril.dot(eri1.T).dot(eri1)
for k in range(nset):
buf1 = buf[0,:naux]
fdrv(ftrans, fmmm,
buf1.ctypes.data_as(ctypes.c_void_p),
eri1.ctypes.data_as(ctypes.c_void_p),
dms[k].ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(naux), *rargs)
buf2 = lib.unpack_tril(eri1, out=buf[1])
vk[k] += lib.dot(buf1.reshape(-1,nao).T, buf2.reshape(-1,nao))
t1 = log.timer_debug1('jk', *t1)
if with_j: vj = lib.unpack_tril(vj, 1).reshape(dm_shape)
if with_k: vk = vk.reshape(dm_shape)
logger.timer(dfobj, 'df vj and vk', *t0)
return vj, vk
[docs]
def get_j(dfobj, dm, hermi=0, direct_scf_tol=1e-13):
from pyscf.scf import _vhf
from pyscf.scf import jk
from pyscf.df import addons
t0 = t1 = (logger.process_clock(), logger.perf_counter())
mol = dfobj.mol
if dfobj._vjopt is None:
dfobj.auxmol = auxmol = addons.make_auxmol(mol, dfobj.auxbasis)
opt = _vhf._VHFOpt(mol, 'int3c2e', 'CVHFnr3c2e_schwarz_cond',
dmcondname='CVHFnr_dm_cond',
direct_scf_tol=direct_scf_tol)
# q_cond part 1: the regular int2e (ij|ij) for mol's basis
opt.init_cvhf_direct(mol, 'int2e', 'CVHFnr_int2e_q_cond')
# Update q_cond to include the 2e-integrals (auxmol|auxmol)
j2c = auxmol.intor('int2c2e', hermi=1)
j2c_diag = numpy.sqrt(abs(j2c.diagonal()))
aux_loc = auxmol.ao_loc
aux_q_cond = [j2c_diag[i0:i1].max()
for i0, i1 in zip(aux_loc[:-1], aux_loc[1:])]
q_cond = numpy.hstack((opt.q_cond.ravel(), aux_q_cond))
opt.q_cond = q_cond
try:
opt.j2c = j2c = scipy.linalg.cho_factor(j2c, lower=True)
opt.j2c_type = 'cd'
except scipy.linalg.LinAlgError:
opt.j2c = j2c
opt.j2c_type = 'regular'
# jk.get_jk function supports 4-index integrals. Use bas_placeholder
# (l=0, nctr=1, 1 function) to hold the last index.
bas_placeholder = numpy.array([0, 0, 1, 1, 0, 0, 0, 0],
dtype=numpy.int32)
fakemol = mol + auxmol
fakemol._bas = numpy.vstack((fakemol._bas, bas_placeholder))
opt.fakemol = fakemol
dfobj._vjopt = opt
t1 = logger.timer_debug1(dfobj, 'df-vj init_direct_scf', *t1)
opt = dfobj._vjopt
fakemol = opt.fakemol
dm = numpy.asarray(dm, order='C')
assert dm.dtype == numpy.float64
dm_shape = dm.shape
nao = dm_shape[-1]
dm = dm.reshape(-1,nao,nao)
n_dm = dm.shape[0]
# First compute the density in auxiliary basis
# j3c = fauxe2(mol, auxmol)
# jaux = numpy.einsum('ijk,ji->k', j3c, dm)
# rho = numpy.linalg.solve(auxmol.intor('int2c2e'), jaux)
nbas = mol.nbas
nbas1 = mol.nbas + dfobj.auxmol.nbas
shls_slice = (0, nbas, 0, nbas, nbas, nbas1, nbas1, nbas1+1)
with lib.temporary_env(opt, prescreen='CVHFnr3c2e_vj_pass1_prescreen'):
jaux = jk.get_jk(fakemol, dm, ['ijkl,ji->kl']*n_dm, 'int3c2e',
aosym='s2ij', hermi=0, shls_slice=shls_slice,
vhfopt=opt)
# remove the index corresponding to bas_placeholder
jaux = numpy.array(jaux)[:,:,0]
t1 = logger.timer_debug1(dfobj, 'df-vj pass 1', *t1)
if opt.j2c_type == 'cd':
rho = scipy.linalg.cho_solve(opt.j2c, jaux.T)
else:
rho = scipy.linalg.solve(opt.j2c, jaux.T)
# transform rho to shape (:,1,naux), to adapt to 3c2e integrals (ij|k)
rho = rho.T[:,numpy.newaxis,:]
t1 = logger.timer_debug1(dfobj, 'df-vj solve ', *t1)
# Next compute the Coulomb matrix
# j3c = fauxe2(mol, auxmol)
# vj = numpy.einsum('ijk,k->ij', j3c, rho)
# temporarily set "_dmcondname=None" to skip the call to set_dm method.
with lib.temporary_env(opt, prescreen='CVHFnr3c2e_vj_pass2_prescreen',
_dmcondname=None):
# CVHFnr3c2e_vj_pass2_prescreen requires custom dm_cond
aux_loc = dfobj.auxmol.ao_loc
dm_cond = [abs(rho[:,:,i0:i1]).max()
for i0, i1 in zip(aux_loc[:-1], aux_loc[1:])]
opt.dm_cond = numpy.array(dm_cond)
vj = jk.get_jk(fakemol, rho, ['ijkl,lk->ij']*n_dm, 'int3c2e',
aosym='s2ij', hermi=1, shls_slice=shls_slice,
vhfopt=opt)
t1 = logger.timer_debug1(dfobj, 'df-vj pass 2', *t1)
logger.timer(dfobj, 'df-vj', *t0)
return numpy.asarray(vj).reshape(dm_shape)
[docs]
def r_get_jk(dfobj, dms, hermi=0, with_j=True, with_k=True):
'''Relativistic density fitting JK'''
t0 = (logger.process_clock(), logger.perf_counter())
mol = dfobj.mol
c1 = .5 / lib.param.LIGHT_SPEED
tao = mol.tmap()
ao_loc = mol.ao_loc_2c()
n2c = ao_loc[-1]
if hermi == 0 and DEBUG:
# J matrix is symmetrized in this function which is only true for
# density matrix with time reversal symmetry
scf.dhf._ensure_time_reversal_symmetry(mol, dms)
def fjk(dm):
dm = numpy.asarray(dm, dtype=numpy.complex128)
fmmm = libri.RIhalfmmm_r_s2_bra_noconj
fdrv = _ao2mo.libao2mo.AO2MOr_e2_drv
ftrans = libri.RItranse2_r_s2
vj = numpy.zeros_like(dm)
vk = numpy.zeros_like(dm)
fcopy = libri.RImmm_r_s2_transpose
rargs = (ctypes.c_int(n2c), (ctypes.c_int*4)(0, n2c, 0, 0),
tao.ctypes.data_as(ctypes.c_void_p),
ao_loc.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(mol.nbas))
dmll = numpy.asarray(dm[:n2c,:n2c], order='C')
dmls = numpy.asarray(dm[:n2c,n2c:], order='C') * c1
dmsl = numpy.asarray(dm[n2c:,:n2c], order='C') * c1
dmss = numpy.asarray(dm[n2c:,n2c:], order='C') * c1**2
for erill, eriss in dfobj.loop():
naux, nao_pair = erill.shape
buf = numpy.empty((naux,n2c,n2c), dtype=numpy.complex128)
buf1 = numpy.empty((naux,n2c,n2c), dtype=numpy.complex128)
fdrv(ftrans, fmmm,
buf.ctypes.data_as(ctypes.c_void_p),
erill.ctypes.data_as(ctypes.c_void_p),
dmll.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(naux), *rargs) # buf == (P|LL)
rho = numpy.einsum('kii->k', buf)
fdrv(ftrans, fcopy,
buf1.ctypes.data_as(ctypes.c_void_p),
erill.ctypes.data_as(ctypes.c_void_p),
dmll.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(naux), *rargs) # buf1 == (P|LL)
vk[:n2c,:n2c] += numpy.dot(buf1.reshape(-1,n2c).T,
buf.reshape(-1,n2c))
fdrv(ftrans, fmmm,
buf.ctypes.data_as(ctypes.c_void_p),
eriss.ctypes.data_as(ctypes.c_void_p),
dmls.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(naux), *rargs) # buf == (P|LS)
vk[:n2c,n2c:] += numpy.dot(buf1.reshape(-1,n2c).T,
buf.reshape(-1,n2c)) * c1
fdrv(ftrans, fmmm,
buf.ctypes.data_as(ctypes.c_void_p),
eriss.ctypes.data_as(ctypes.c_void_p),
dmss.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(naux), *rargs) # buf == (P|SS)
rho += numpy.einsum('kii->k', buf)
vj[:n2c,:n2c] += lib.unpack_tril(numpy.dot(rho, erill), 1)
vj[n2c:,n2c:] += lib.unpack_tril(numpy.dot(rho, eriss), 1) * c1**2
fdrv(ftrans, fcopy,
buf1.ctypes.data_as(ctypes.c_void_p),
eriss.ctypes.data_as(ctypes.c_void_p),
dmss.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(naux), *rargs) # buf == (P|SS)
vk[n2c:,n2c:] += numpy.dot(buf1.reshape(-1,n2c).T,
buf.reshape(-1,n2c)) * c1**2
if hermi != 1:
fdrv(ftrans, fmmm,
buf.ctypes.data_as(ctypes.c_void_p),
erill.ctypes.data_as(ctypes.c_void_p),
dmsl.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(naux), *rargs) # buf == (P|SL)
vk[n2c:,:n2c] += numpy.dot(buf1.reshape(-1,n2c).T,
buf.reshape(-1,n2c)) * c1
if hermi == 1:
vk[n2c:,:n2c] = vk[:n2c,n2c:].T.conj()
return vj, vk
if isinstance(dms, numpy.ndarray) and dms.ndim == 2:
vj, vk = fjk(dms)
else:
vjk = [fjk(dm) for dm in dms]
vj = numpy.array([x[0] for x in vjk])
vk = numpy.array([x[1] for x in vjk])
logger.timer(dfobj, 'vj and vk', *t0)
return vj, vk
if __name__ == '__main__':
import pyscf.gto
import pyscf.scf
mol = pyscf.gto.Mole()
mol.build(
verbose = 0,
atom = [["O" , (0. , 0. , 0.)],
[1 , (0. , -0.757 , 0.587)],
[1 , (0. , 0.757 , 0.587)] ],
basis = 'ccpvdz',
)
method = density_fit(pyscf.scf.RHF(mol), 'weigend')
method.max_memory = 0
energy = method.scf()
print(energy, -76.0259362997)
method = density_fit(pyscf.scf.DHF(mol), 'weigend')
energy = method.scf()
print(energy, -76.0807386770) # normal DHF energy is -76.0815679438127
method = density_fit(pyscf.scf.UKS(mol), 'weigend', only_dfj = True)
energy = method.scf()
print(energy, -75.8547753298)
mol.build(
verbose = 0,
atom = [["O" , (0. , 0. , 0.)],
[1 , (0. , -0.757 , 0.587)],
[1 , (0. , 0.757 , 0.587)] ],
basis = 'ccpvdz',
spin = 1,
charge = 1,
)
method = density_fit(pyscf.scf.UHF(mol), 'weigend')
energy = method.scf()
print(energy, -75.6310072359)
method = density_fit(pyscf.scf.RHF(mol), 'weigend')
energy = method.scf()
print(energy, -75.6265157244)
