#!/usr/bin/env python
# Copyright 2014-2021 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>
#
'''
Solve CISD equation H C = C e where e = E_HF + E_CORR
'''
from functools import reduce
import numpy
from pyscf import gto
from pyscf import lib
from pyscf.lib import logger
from pyscf.cc import ccsd
from pyscf.cc import ccsd_rdm
from pyscf.fci import cistring
from pyscf import __config__
BLKMIN = getattr(__config__, 'ci_cisd_blkmin', 4)
[docs]
def kernel(myci, eris, ci0=None, max_cycle=50, tol=1e-8, verbose=logger.INFO):
'''
Run CISD calculation.
Args:
myci : CISD (inheriting) object
eris : ccsd._ChemistsERIs (inheriting) object (poss diff for df)
Contains the various (pq|rs) integrals needed.
Kwargs:
ci0 : (List of) numpy array(s) (if None it will set)
Initial guess for CISD coeffs.
max_cycle : integer
Maximum number of iterations to converge to CISD solution.
If not converged before, calculation stops without having
converged.
tol : float
Convergence tolerance.
verbose : integer
Level of output (roughly: the higher, the more output).
Returns:
conv : bool
Is it converged?
ecisd : List of floats or float
The lowest :attr:`myci.nroots` eigenvalues.
ci : List of 1D arrays or 1D array
The lowest :attr:`myci.nroots` eigenvectors.
'''
log = logger.new_logger(myci, verbose)
diag = myci.make_diagonal(eris)
# Note that ehf is not the HF energy (see `make_diagonal`).
ehf = diag[0]
diag -= ehf
if ci0 is None:
ci0 = myci.get_init_guess(eris=eris, nroots=myci.nroots, diag=diag)[1]
def op(xs):
return [myci.contract(x, eris) for x in xs]
def precond(x, e, *args):
diagd = diag - (e-myci.level_shift)
diagd[abs(diagd)<1e-8] = 1e-8
return x / diagd
if myci._dot is not None:
nmo = myci.nmo
nocc = myci.nocc
def cisd_dot(x1, x2):
return myci._dot(x1, x2, nmo, nocc)
else:
cisd_dot = numpy.dot
conv, ecisd, ci = lib.davidson1(op, ci0, precond, tol=tol,
max_cycle=max_cycle, max_space=myci.max_space,
lindep=myci.lindep, dot=cisd_dot,
nroots=myci.nroots, verbose=log)
if myci.nroots == 1:
conv = conv[0]
ecisd = ecisd[0]
ci = ci[0]
return conv, ecisd, ci
[docs]
def make_diagonal(myci, eris):
'''
Return diagonal of CISD hamiltonian in Slater determinant basis.
Note that a constant has been substracted of all elements.
The first element is the HF energy (minus the
constant), the next elements are the diagonal elements with singly
excited determinants (<D_i^a|H|D_i^a> within the constant), then
doubly excited determinants (<D_ij^ab|H|D_ij^ab> within the
constant).
Args:
myci : CISD (inheriting) object
eris : ccsd._ChemistsERIs (inheriting) object (poss diff for df)
Contains the various (pq|rs) integrals needed.
Returns:
numpy array (size: (1, 1 + #single excitations from HF det
+ #double excitations from HF det))
Diagonal elements of hamiltonian matrix within a constant,
see above.
'''
# DO NOT use eris.mo_energy, it may differ to eris.fock.diagonal()
mo_energy = eris.fock.diagonal()
nmo = mo_energy.size
jdiag = numpy.zeros((nmo,nmo))
kdiag = numpy.zeros((nmo,nmo))
nocc = eris.nocc
nvir = nmo - nocc
jdiag[:nocc,:nocc] = numpy.einsum('iijj->ij', eris.oooo)
kdiag[:nocc,:nocc] = numpy.einsum('jiij->ij', eris.oooo)
jdiag[:nocc,nocc:] = numpy.einsum('iijj->ij', eris.oovv)
kdiag[:nocc,nocc:] = numpy.einsum('ijji->ij', eris.ovvo)
if eris.vvvv is not None and len(eris.vvvv.shape) == 2:
#:eris_vvvv = ao2mo.restore(1, eris.vvvv, nvir)
#:jdiag1 = numpy.einsum('iijj->ij', eris_vvvv)
diag_idx = numpy.arange(nvir)
diag_idx = diag_idx * (diag_idx + 1) // 2 + diag_idx
for i, ii in enumerate(diag_idx):
jdiag[nocc+i,nocc:] = eris.vvvv[ii][diag_idx]
jksum = (jdiag[:nocc,:nocc] * 2 - kdiag[:nocc,:nocc]).sum()
# Note that ehf is not the HF energy.
ehf = mo_energy[:nocc].sum() * 2 - jksum
e_ia = lib.direct_sum('a-i->ia', mo_energy[nocc:], mo_energy[:nocc])
e_ia -= jdiag[:nocc,nocc:] - kdiag[:nocc,nocc:]
e1diag = ehf + e_ia
e2diag = lib.direct_sum('ia+jb->ijab', e_ia, e_ia)
e2diag += ehf
e2diag += jdiag[:nocc,:nocc].reshape(nocc,nocc,1,1)
e2diag -= jdiag[:nocc,nocc:].reshape(nocc,1,1,nvir)
e2diag -= jdiag[:nocc,nocc:].reshape(1,nocc,nvir,1)
e2diag += jdiag[nocc:,nocc:].reshape(1,1,nvir,nvir)
return numpy.hstack((ehf, e1diag.reshape(-1), e2diag.reshape(-1)))
[docs]
def contract(myci, civec, eris):
'''
Application of CISD hamiltonian onto civec.
Args:
myci : CISD (inheriting) object
civec : numpy array, same length as a CI vector.
eris : ccsd._ChemistsERIs (inheriting) object (poss diff for df)
Contains the various (pq|rs) integrals needed.
Returns:
numpy array, same length as a CI vector.
'''
time0 = logger.process_clock(), logger.perf_counter()
log = logger.Logger(myci.stdout, myci.verbose)
nocc = myci.nocc
nmo = myci.nmo
nvir = nmo - nocc
c0, c1, c2 = myci.cisdvec_to_amplitudes(civec, nmo, nocc, copy=False)
t2 = myci._add_vvvv(c2, eris, t2sym='jiba')
t2 *= .5 # due to t2+t2.transpose(1,0,3,2) in the end
log.timer_debug1('vvvv', *time0)
foo = eris.fock[:nocc,:nocc].copy()
fov = eris.fock[:nocc,nocc:].copy()
fvv = eris.fock[nocc:,nocc:].copy()
t1 = fov * c0
t1 += numpy.einsum('ib,ab->ia', c1, fvv)
t1 -= numpy.einsum('ja,ji->ia', c1, foo)
t2 += lib.einsum('kilj,klab->ijab', _cp(eris.oooo)*.5, c2)
t2 += lib.einsum('ijac,bc->ijab', c2, fvv)
t2 -= lib.einsum('kj,kiba->jiba', foo, c2)
t2 += numpy.einsum('ia,jb->ijab', c1, fov)
unit = nocc*nvir**2 + nocc**2*nvir*3 + 1
max_memory = max(0, myci.max_memory - lib.current_memory()[0])
blksize = min(nvir, max(BLKMIN, int(max_memory*.9e6/8/unit)))
log.debug1('max_memory %d MB, nocc,nvir = %d,%d blksize = %d',
max_memory, nocc, nvir, blksize)
for p0, p1 in lib.prange(0, nvir, blksize):
eris_oVoV = _cp(_cp(eris.oovv[:,:,p0:p1]).transpose(0,2,1,3))
tmp = lib.einsum('kbjc,ikca->jiba', eris_oVoV, c2)
t2[:,:,p0:p1] -= tmp*.5
t2[:,:,p0:p1] -= tmp.transpose(1,0,2,3)
tmp = None
eris_ovvo = _cp(eris.ovvo[:,p0:p1])
t2[:,:,p0:p1] += eris_ovvo.transpose(0,3,1,2) * (c0*.5)
t1 += numpy.einsum('ia,iabj->jb', c1[:,p0:p1], eris_ovvo) * 2
t1[:,p0:p1] -= numpy.einsum('ib,iajb->ja', c1, eris_oVoV)
ovov = -.5 * eris_oVoV
ovov += eris_ovvo.transpose(3,1,0,2)
eris_oVoV = None
theta = c2[:,:,p0:p1].transpose(2,0,1,3) * 2
theta-= c2[:,:,p0:p1].transpose(2,1,0,3)
for j in range(nocc):
t2[:,j] += lib.einsum('ckb,ckia->iab', ovov[j], theta)
tmp = ovov = None
t1 += numpy.einsum('aijb,ia->jb', theta, fov[:,p0:p1])
eris_ovoo = _cp(eris.ovoo[:,p0:p1])
t1 -= lib.einsum('bjka,jbki->ia', theta, eris_ovoo)
t2[:,:,p0:p1] -= lib.einsum('jbik,ka->jiba', eris_ovoo.conj(), c1)
eris_ovoo = None
eris_ovvv = eris.get_ovvv(slice(None), slice(p0,p1)).conj()
t1 += lib.einsum('cjib,jcba->ia', theta, eris_ovvv)
t2[:,:,p0:p1] += lib.einsum('iacb,jc->ijab', eris_ovvv, c1)
tmp = eris_ovvv = None
#:t2 + t2.transpose(1,0,3,2)
for i in range(nocc):
if i > 0:
t2[i,:i]+= t2[:i,i].transpose(0,2,1)
t2[:i,i] = t2[i,:i].transpose(0,2,1)
t2[i,i] = t2[i,i] + t2[i,i].T
t0 = numpy.einsum('ia,ia->', fov, c1) * 2
t0 += numpy.einsum('iabj,ijab->', eris.ovvo, c2) * 2
t0 -= numpy.einsum('iabj,jiab->', eris.ovvo, c2)
cinew = myci.amplitudes_to_cisdvec(t0, t1, t2)
return cinew
[docs]
def amplitudes_to_cisdvec(c0, c1, c2):
return numpy.hstack((c0, c1.ravel(), c2.ravel()))
[docs]
def cisdvec_to_amplitudes(civec, nmo, nocc, copy=True):
nvir = nmo - nocc
c0 = civec[0]
cp = lambda x: (x.copy() if copy else x)
c1 = cp(civec[1:nocc*nvir+1].reshape(nocc,nvir))
c2 = cp(civec[nocc*nvir+1:].reshape(nocc,nocc,nvir,nvir))
return c0, c1, c2
[docs]
def dot(v1, v2, nmo, nocc):
nvir = nmo - nocc
hijab = v2[1+nocc*nvir:].reshape(nocc,nocc,nvir,nvir)
cijab = v1[1+nocc*nvir:].reshape(nocc,nocc,nvir,nvir)
val = numpy.dot(v1, v2) * 2 - v1[0]*v2[0]
val-= numpy.einsum('jiab,ijab->', cijab, hijab)
return val
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def t1strs(norb, nelec):
'''Compute the FCI strings (address) for CIS single-excitation amplitudes
and the signs of the coefficients when transferring the reference from
physics vacuum to HF vacuum.
'''
addrs, signs = tn_addrs_signs(norb, nelec, 1)
return addrs, signs
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def tn_addrs_signs(norb, nelec, n_excite):
'''Compute the FCI strings (address) for CIS n-excitation amplitudes and
the signs of the coefficients when transferring the reference from physics
vacuum to HF vacuum.
If the excitation level is not compatible with the number of
electrons and holes, empty lists are returned for the addresses and signs.
'''
# Not enough electrons or holes for excitation; return empty lists.
if n_excite > min(nelec, norb-nelec):
return [], []
nocc = nelec
hole_strs = cistring.gen_strings4orblist(range(nocc), nocc - n_excite)
# For HF vacuum, hole operators are ordered from low-lying to high-lying
# orbitals. It leads to the opposite string ordering.
hole_strs = hole_strs[::-1]
hole_sum = numpy.zeros(len(hole_strs), dtype=int)
for i in range(nocc):
hole_at_i = (hole_strs & (1 << i)) == 0
hole_sum[hole_at_i] += i
# The hole operators are listed from low-lying to high-lying orbitals
# (from left to right). For i-th (0-based) hole operator, the number of
# orbitals which are higher than i determines the sign. This number
# equals to nocc-(i+1). After removing the highest hole operator, nocc
# becomes nocc-1, the sign for next hole operator j will be associated to
# nocc-1-(j+1). By iteratively calling this procedure, the overall sign
# for annihilating three holes is (-1)**(3*nocc - 6 - sum i)
sign = (-1) ** (n_excite * nocc - n_excite*(n_excite+1)//2 - hole_sum)
particle_strs = cistring.gen_strings4orblist(range(nocc, norb), n_excite)
strs = hole_strs[:,None] ^ particle_strs
addrs = cistring.strs2addr(norb, nocc, strs.ravel())
signs = numpy.vstack([sign] * len(particle_strs)).T.ravel()
return addrs, signs
[docs]
def to_fcivec(cisdvec, norb, nelec, frozen=None):
'''Convert CISD coefficients to FCI coefficients'''
if isinstance(nelec, (int, numpy.number)):
nelecb = nelec//2
neleca = nelec - nelecb
else:
neleca, nelecb = nelec
assert (neleca == nelecb)
frozen_mask = numpy.zeros(norb, dtype=bool)
if frozen is None:
nfroz = 0
elif isinstance(frozen, (int, numpy.integer)):
nfroz = frozen
frozen_mask[:frozen] = True
else:
nfroz = len(frozen)
frozen_mask[frozen] = True
nocc = numpy.count_nonzero(~frozen_mask[:neleca])
nmo = norb - nfroz
nvir = nmo - nocc
c0, c1, c2 = cisdvec_to_amplitudes(cisdvec, nmo, nocc, copy=False)
t1addr, t1sign = tn_addrs_signs(nmo, nocc, 1)
na = cistring.num_strings(nmo, nocc)
fcivec = numpy.zeros((na,na))
fcivec[0,0] = c0
fcivec[0,t1addr] = fcivec[t1addr,0] = c1.ravel() * t1sign
c2ab = c2.transpose(0,2,1,3).reshape(nocc*nvir,-1)
c2ab = numpy.einsum('i,j,ij->ij', t1sign, t1sign, c2ab)
fcivec[t1addr[:,None],t1addr] = c2ab
if nocc > 1 and nvir > 1:
c2aa = c2 - c2.transpose(1,0,2,3)
ooidx = numpy.tril_indices(nocc, -1)
vvidx = numpy.tril_indices(nvir, -1)
c2aa = c2aa[ooidx][:,vvidx[0],vvidx[1]]
t2addr, t2sign = tn_addrs_signs(nmo, nocc, 2)
fcivec[0,t2addr] = fcivec[t2addr,0] = c2aa.ravel() * t2sign
if nfroz == 0:
return fcivec
assert (norb < 63)
strs = cistring.gen_strings4orblist(range(norb), neleca)
na = len(strs)
count = numpy.zeros(na, dtype=int)
parity = numpy.zeros(na, dtype=bool)
core_mask = numpy.ones(na, dtype=bool)
# During the loop, count saves the number of occupied orbitals that
# lower (with small orbital ID) than the present orbital i.
# Moving all the frozen orbitals to the beginning of the orbital list
# (before the occupied orbitals) leads to parity odd (= True, with
# negative sign) or even (= False, with positive sign).
for i in range(norb):
if frozen_mask[i]:
if i < neleca:
# frozen occupied orbital should be occupied
core_mask &= (strs & (1 << i)) != 0
parity ^= (count & 1) == 1
else:
# frozen virtual orbital should not be occupied.
# parity is not needed since it's unoccupied
core_mask &= (strs & (1 << i)) == 0
else:
count += (strs & (1 << i)) != 0
sub_strs = strs[core_mask & (count == nocc)]
addrs = cistring.strs2addr(norb, neleca, sub_strs)
fcivec1 = numpy.zeros((na,na))
fcivec1[addrs[:,None],addrs] = fcivec
fcivec1[parity,:] *= -1
fcivec1[:,parity] *= -1
return fcivec1
[docs]
def from_fcivec(ci0, norb, nelec, frozen=None):
'''Extract CISD coefficients from FCI coefficients'''
if not (frozen is None or frozen == 0):
raise NotImplementedError
if isinstance(nelec, (int, numpy.number)):
nelecb = nelec//2
neleca = nelec - nelecb
else:
neleca, nelecb = nelec
nocc = neleca
nvir = norb - nocc
t1addr, t1sign = t1strs(norb, nocc)
c0 = ci0[0,0]
c1 = ci0[0,t1addr] * t1sign
c2 = numpy.einsum('i,j,ij->ij', t1sign, t1sign, ci0[t1addr[:,None],t1addr])
c1 = c1.reshape(nocc,nvir)
c2 = c2.reshape(nocc,nvir,nocc,nvir).transpose(0,2,1,3)
return amplitudes_to_cisdvec(c0, c1, c2)
[docs]
def overlap(cibra, ciket, nmo, nocc, s=None):
'''Overlap between two CISD wavefunctions.
Args:
s : 2D array
The overlap matrix of non-orthogonal one-particle basis
'''
if s is None:
return dot(cibra, ciket, nmo, nocc)
DEBUG = True
nvir = nmo - nocc
nov = nocc * nvir
bra0, bra1, bra2 = cisdvec_to_amplitudes(cibra, nmo, nocc, copy=False)
ket0, ket1, ket2 = cisdvec_to_amplitudes(ciket, nmo, nocc, copy=False)
# Sort the ket orbitals to make the orbitals in bra one-one mapt to orbitals
# in ket.
if ((not DEBUG) and
abs(numpy.linalg.det(s[:nocc,:nocc]) - 1) < 1e-2 and
abs(numpy.linalg.det(s[nocc:,nocc:]) - 1) < 1e-2):
ket_orb_idx = numpy.where(abs(s) > 0.9)[1]
s = s[:,ket_orb_idx]
oidx = ket_orb_idx[:nocc]
vidx = ket_orb_idx[nocc:] - nocc
ket1 = ket1[oidx[:,None],vidx]
ket2 = ket2[oidx[:,None,None,None],oidx[:,None,None],vidx[:,None],vidx]
ooidx = numpy.tril_indices(nocc, -1)
vvidx = numpy.tril_indices(nvir, -1)
bra2aa = bra2 - bra2.transpose(1,0,2,3)
bra2aa = lib.take_2d(bra2aa.reshape(nocc**2,nvir**2),
ooidx[0]*nocc+ooidx[1], vvidx[0]*nvir+vvidx[1])
ket2aa = ket2 - ket2.transpose(1,0,2,3)
ket2aa = lib.take_2d(ket2aa.reshape(nocc**2,nvir**2),
ooidx[0]*nocc+ooidx[1], vvidx[0]*nvir+vvidx[1])
occlist0 = numpy.arange(nocc).reshape(1,nocc)
occlists = numpy.repeat(occlist0, 1+nov+bra2aa.size, axis=0)
occlist0 = occlists[:1]
occlist1 = occlists[1:1+nov]
occlist2 = occlists[1+nov:]
ia = 0
for i in range(nocc):
for a in range(nocc, nmo):
occlist1[ia,i] = a
ia += 1
ia = 0
for i in range(nocc):
for j in range(i):
for a in range(nocc, nmo):
for b in range(nocc, a):
occlist2[ia,i] = a
occlist2[ia,j] = b
ia += 1
na = len(occlists)
if DEBUG:
trans = numpy.empty((na,na))
for i, idx in enumerate(occlists):
s_sub = s[idx].T.copy()
minors = s_sub[occlists]
trans[i,:] = numpy.linalg.det(minors)
# Mimic the transformation einsum('ab,ap->pb', FCI, trans).
# The wavefunction FCI has the [excitation_alpha,excitation_beta]
# representation. The zero blocks like FCI[S_alpha,D_beta],
# FCI[D_alpha,D_beta], are explicitly excluded.
bra_mat = numpy.zeros((na,na))
bra_mat[0,0] = bra0
bra_mat[0,1:1+nov] = bra_mat[1:1+nov,0] = bra1.ravel()
bra_mat[0,1+nov:] = bra_mat[1+nov:,0] = bra2aa.ravel()
bra_mat[1:1+nov,1:1+nov] = bra2.transpose(0,2,1,3).reshape(nov,nov)
ket_mat = numpy.zeros((na,na))
ket_mat[0,0] = ket0
ket_mat[0,1:1+nov] = ket_mat[1:1+nov,0] = ket1.ravel()
ket_mat[0,1+nov:] = ket_mat[1+nov:,0] = ket2aa.ravel()
ket_mat[1:1+nov,1:1+nov] = ket2.transpose(0,2,1,3).reshape(nov,nov)
ovlp = lib.einsum('ab,ap,bq,pq->', bra_mat, trans, trans, ket_mat)
else:
nov1 = 1 + nov
noovv = bra2aa.size
bra_SS = numpy.zeros((nov1,nov1))
bra_SS[0,0] = bra0
bra_SS[0,1:] = bra_SS[1:,0] = bra1.ravel()
bra_SS[1:,1:] = bra2.transpose(0,2,1,3).reshape(nov,nov)
ket_SS = numpy.zeros((nov1,nov1))
ket_SS[0,0] = ket0
ket_SS[0,1:] = ket_SS[1:,0] = ket1.ravel()
ket_SS[1:,1:] = ket2.transpose(0,2,1,3).reshape(nov,nov)
trans_SS = numpy.empty((nov1,nov1))
trans_SD = numpy.empty((nov1,noovv))
trans_DS = numpy.empty((noovv,nov1))
occlist01 = occlists[:nov1]
for i, idx in enumerate(occlist01):
s_sub = s[idx].T.copy()
minors = s_sub[occlist01]
trans_SS[i,:] = numpy.linalg.det(minors)
minors = s_sub[occlist2]
trans_SD[i,:] = numpy.linalg.det(minors)
s_sub = s[:,idx].copy()
minors = s_sub[occlist2]
trans_DS[:,i] = numpy.linalg.det(minors)
ovlp = lib.einsum('ab,ap,bq,pq->', bra_SS, trans_SS, trans_SS, ket_SS)
ovlp+= lib.einsum('ab,a ,bq, q->', bra_SS, trans_SS[:,0], trans_SD, ket2aa.ravel())
ovlp+= lib.einsum('ab,ap,b ,p ->', bra_SS, trans_SD, trans_SS[:,0], ket2aa.ravel())
ovlp+= lib.einsum(' b, p,bq,pq->', bra2aa.ravel(), trans_SS[0,:], trans_DS, ket_SS)
ovlp+= lib.einsum(' b, p,b ,p ->', bra2aa.ravel(), trans_SD[0,:], trans_DS[:,0],
ket2aa.ravel())
ovlp+= lib.einsum('a ,ap, q,pq->', bra2aa.ravel(), trans_DS, trans_SS[0,:], ket_SS)
ovlp+= lib.einsum('a ,a , q, q->', bra2aa.ravel(), trans_DS[:,0], trans_SD[0,:],
ket2aa.ravel())
# FIXME: whether to approximate the overlap between double excitation coefficients
if numpy.linalg.norm(bra2aa)*numpy.linalg.norm(ket2aa) < 1e-4:
# Skip the overlap if coefficients of double excitation are small enough
pass
if (abs(numpy.linalg.det(s[:nocc,:nocc]) - 1) < 1e-2 and
abs(numpy.linalg.det(s[nocc:,nocc:]) - 1) < 1e-2):
# If the overlap matrix close to identity enough, use the <D|D'> overlap
# for orthogonal single-particle basis to approximate the overlap
# for non-orthogonal basis.
ovlp+= numpy.dot(bra2aa.ravel(), ket2aa.ravel()) * trans_SS[0,0] * 2
else:
from multiprocessing import sharedctypes, Process
buf_ctypes = sharedctypes.RawArray('d', noovv)
trans_ket = numpy.ndarray(noovv, buffer=buf_ctypes)
def trans_dot_ket(i0, i1):
for i in range(i0, i1):
s_sub = s[occlist2[i]].T.copy()
minors = s_sub[occlist2]
trans_ket[i] = numpy.linalg.det(minors).dot(ket2aa.ravel())
nproc = lib.num_threads()
if nproc > 1:
seg = (noovv+nproc-1) // nproc
ps = []
for i0,i1 in lib.prange(0, noovv, seg):
p = Process(target=trans_dot_ket, args=(i0,i1))
ps.append(p)
p.start()
[p.join() for p in ps]
else:
trans_dot_ket(0, noovv)
ovlp+= numpy.dot(bra2aa.ravel(), trans_ket) * trans_SS[0,0] * 2
return ovlp
[docs]
def make_rdm1(myci, civec=None, nmo=None, nocc=None, ao_repr=False):
r'''
Spin-traced one-particle density matrix in MO basis (the occupied-virtual
blocks from the orbital response contribution are not included).
dm1[p,q] = <q_alpha^\dagger p_alpha> + <q_beta^\dagger p_beta>
The convention of 1-pdm is based on McWeeney's book, Eq (5.4.20).
The contraction between 1-particle Hamiltonian and rdm1 is
E = einsum('pq,qp', h1, rdm1)
'''
if civec is None: civec = myci.ci
if nmo is None: nmo = myci.nmo
if nocc is None: nocc = myci.nocc
d1 = _gamma1_intermediates(myci, civec, nmo, nocc)
return ccsd_rdm._make_rdm1(myci, d1, with_frozen=True, ao_repr=ao_repr)
[docs]
def make_rdm2(myci, civec=None, nmo=None, nocc=None, ao_repr=False):
r'''
Spin-traced two-particle density matrix in MO basis
dm2[p,q,r,s] = \sum_{sigma,tau} <p_sigma^\dagger r_tau^\dagger s_tau q_sigma>
Note the contraction between ERIs (in Chemist's notation) and rdm2 is
E = einsum('pqrs,pqrs', eri, rdm2)
'''
if civec is None: civec = myci.ci
if nmo is None: nmo = myci.nmo
if nocc is None: nocc = myci.nocc
d1 = _gamma1_intermediates(myci, civec, nmo, nocc)
f = lib.H5TmpFile()
d2 = _gamma2_outcore(myci, civec, nmo, nocc, f, False)
return ccsd_rdm._make_rdm2(myci, d1, d2, with_dm1=True, with_frozen=True,
ao_repr=ao_repr)
def _gamma1_intermediates(myci, civec, nmo, nocc):
c0, c1, c2 = myci.cisdvec_to_amplitudes(civec, nmo, nocc, copy=False)
dvo = c0.conj() * c1.T
dvo += numpy.einsum('jb,ijab->ai', c1.conj(), c2) * 2
dvo -= numpy.einsum('jb,ijba->ai', c1.conj(), c2)
dov = dvo.T.conj()
theta = c2*2 - c2.transpose(0,1,3,2)
doo = -numpy.einsum('ia,ka->ik', c1.conj(), c1)
doo -= lib.einsum('ijab,ikab->jk', c2.conj(), theta)
dvv = numpy.einsum('ia,ic->ac', c1, c1.conj())
dvv += lib.einsum('ijab,ijac->bc', theta, c2.conj())
return doo, dov, dvo, dvv
def _gamma2_intermediates(myci, civec, nmo, nocc, compress_vvvv=False):
f = lib.H5TmpFile()
_gamma2_outcore(myci, civec, nmo, nocc, f, compress_vvvv)
d2 = (f['dovov'][:], f['dvvvv'][:], f['doooo'][:], f['doovv'][:],
f['dovvo'][:], None, f['dovvv'][:], f['dooov'][:])
return d2
def _gamma2_outcore(myci, civec, nmo, nocc, h5fobj, compress_vvvv=False):
log = logger.Logger(myci.stdout, myci.verbose)
nocc = myci.nocc
nmo = myci.nmo
nvir = nmo - nocc
nvir_pair = nvir * (nvir+1) // 2
c0, c1, c2 = myci.cisdvec_to_amplitudes(civec, nmo, nocc, copy=False)
h5fobj['dovov'] = (2*c0*c2.conj().transpose(0,2,1,3) -
c0*c2.conj().transpose(1,2,0,3))
doooo = lib.einsum('ijab,klab->ijkl', c2.conj(), c2)
h5fobj['doooo'] = doooo.transpose(0,2,1,3) - doooo.transpose(1,2,0,3)*.5
doooo = None
dooov = -lib.einsum('ia,klac->klic', c1*2, c2.conj())
h5fobj['dooov'] = dooov.transpose(0,2,1,3)*2 - dooov.transpose(1,2,0,3)
dooov = None
#:dvovv = numpy.einsum('ia,ikcd->akcd', c1, c2) * 2
#:dvvvv = lib.einsum('ijab,ijcd->abcd', c2, c2)
max_memory = max(0, myci.max_memory - lib.current_memory()[0])
unit = max(nocc**2*nvir*2+nocc*nvir**2*3 + 1, nvir**3*2+nocc*nvir**2 + 1)
blksize = min(nvir, max(BLKMIN, int(max_memory*.9e6/8/unit)))
log.debug1('rdm intermediates: block size = %d, nvir = %d in %d blocks',
blksize, nocc, int((nvir+blksize-1)/blksize))
dtype = numpy.result_type(civec).char
dovvv = h5fobj.create_dataset('dovvv', (nocc,nvir,nvir,nvir), dtype,
chunks=(nocc,min(nocc,nvir),1,nvir))
if compress_vvvv:
dvvvv = h5fobj.create_dataset('dvvvv', (nvir_pair,nvir_pair), dtype)
else:
dvvvv = h5fobj.create_dataset('dvvvv', (nvir,nvir,nvir,nvir), dtype)
for (p0, p1) in lib.prange(0, nvir, blksize):
theta = c2[:,:,p0:p1] - c2[:,:,p0:p1].transpose(1,0,2,3) * .5
gvvvv = lib.einsum('ijab,ijcd->abcd', theta.conj(), c2)
if compress_vvvv:
# symmetrize dvvvv because it does not affect the results of cisd_grad
# dvvvv = (dvvvv+dvvvv.transpose(0,1,3,2)) * .5
# dvvvv = (dvvvv+dvvvv.transpose(1,0,2,3)) * .5
# now dvvvv == dvvvv.transpose(0,1,3,2) == dvvvv.transpose(1,0,3,2)
tmp = numpy.empty((nvir,nvir,nvir))
tmpvvvv = numpy.empty((p1-p0,nvir,nvir_pair))
for i in range(p1-p0):
tmp[:] = gvvvv[i].conj().transpose(1,0,2)
lib.pack_tril(tmp+tmp.transpose(0,2,1), out=tmpvvvv[i])
# tril of (dvvvv[p0:p1,p0:p1]+dvvvv[p0:p1,p0:p1].T)
for i in range(p0, p1):
for j in range(p0, i):
tmpvvvv[i-p0,j] += tmpvvvv[j-p0,i]
tmpvvvv[i-p0,i] *= 2
for i in range(p1, nvir):
off = i * (i+1) // 2
dvvvv[off+p0:off+p1] = tmpvvvv[:,i]
for i in range(p0, p1):
off = i * (i+1) // 2
if p0 > 0:
tmpvvvv[i-p0,:p0] += dvvvv[off:off+p0]
dvvvv[off:off+i+1] = tmpvvvv[i-p0,:i+1] * .25
tmp = tmpvvvv = None
else:
for i in range(p0, p1):
dvvvv[i] = gvvvv[i-p0].conj().transpose(1,0,2)
gvovv = numpy.einsum('ia,ikcd->akcd', c1[:,p0:p1].conj()*2, c2)
gvovv = gvovv.conj()
dovvv[:,:,p0:p1] = gvovv.transpose(1,3,0,2)*2 - gvovv.transpose(1,2,0,3)
theta = c2*2 - c2.transpose(1,0,2,3)
doovv = numpy.einsum('ia,kc->ikca', c1.conj(), -c1)
doovv -= lib.einsum('kjcb,kica->jiab', c2.conj(), theta)
doovv -= lib.einsum('ikcb,jkca->ijab', c2.conj(), theta)
h5fobj['doovv'] = doovv
doovv = None
dovvo = lib.einsum('ikac,jkbc->iabj', theta.conj(), theta)
dovvo += numpy.einsum('ia,kc->iack', c1.conj(), c1) * 2
h5fobj['dovvo'] = dovvo
theta = dovvo = None
dvvov = None
return (h5fobj['dovov'], h5fobj['dvvvv'], h5fobj['doooo'], h5fobj['doovv'],
h5fobj['dovvo'], dvvov , h5fobj['dovvv'], h5fobj['dooov'])
[docs]
def trans_rdm1(myci, cibra, ciket, nmo=None, nocc=None):
r'''
Spin-traced one-particle transition density matrix in MO basis.
dm1[p,q] = <q_alpha^\dagger p_alpha> + <q_beta^\dagger p_beta>
The convention of 1-pdm is based on McWeeney's book, Eq (5.4.20).
The contraction between 1-particle Hamiltonian and rdm1 is
E = einsum('pq,qp', h1, rdm1)
'''
if nmo is None: nmo = myci.nmo
if nocc is None: nocc = myci.nocc
c0bra, c1bra, c2bra = myci.cisdvec_to_amplitudes(cibra, nmo, nocc, copy=False)
c0ket, c1ket, c2ket = myci.cisdvec_to_amplitudes(ciket, nmo, nocc, copy=False)
dvo = c0bra.conj() * c1ket.T
dvo += numpy.einsum('jb,ijab->ai', c1bra.conj(), c2ket) * 2
dvo -= numpy.einsum('jb,ijba->ai', c1bra.conj(), c2ket)
dov = c0ket * c1bra.conj()
dov += numpy.einsum('jb,ijab->ia', c1ket, c2bra.conj()) * 2
dov -= numpy.einsum('jb,ijba->ia', c1ket, c2bra.conj())
theta = c2ket*2 - c2ket.transpose(0,1,3,2)
doo = -numpy.einsum('ia,ka->ik', c1bra.conj(), c1ket)
doo -= lib.einsum('ijab,ikab->jk', c2bra.conj(), theta)
dvv = numpy.einsum('ia,ic->ac', c1ket, c1bra.conj())
dvv += lib.einsum('ijab,ijac->bc', theta, c2bra.conj())
dm1 = numpy.empty((nmo,nmo), dtype=doo.dtype)
dm1[:nocc,:nocc] = doo * 2
dm1[:nocc,nocc:] = dov * 2
dm1[nocc:,:nocc] = dvo * 2
dm1[nocc:,nocc:] = dvv * 2
norm = dot(cibra, ciket, nmo, nocc)
dm1[numpy.diag_indices(nocc)] += 2 * norm
if myci.frozen is not None:
nmo = myci.mo_occ.size
nocc = numpy.count_nonzero(myci.mo_occ > 0)
rdm1 = numpy.zeros((nmo,nmo), dtype=dm1.dtype)
rdm1[numpy.diag_indices(nocc)] = 2 * norm
moidx = numpy.where(myci.get_frozen_mask())[0]
rdm1[moidx[:,None],moidx] = dm1
dm1 = rdm1
return dm1
[docs]
def as_scanner(ci):
'''Generating a scanner/solver for CISD PES.
The returned solver is a function. This function requires one argument
"mol" as input and returns total CISD energy.
The solver will automatically use the results of last calculation as the
initial guess of the new calculation. All parameters assigned in the
CISD and the underlying SCF objects (conv_tol, max_memory etc) are
automatically applied in the solver.
Note scanner has side effects. It may change many underlying objects
(_scf, with_df, with_x2c, ...) during calculation.
Examples::
>>> from pyscf import gto, scf, ci
>>> mol = gto.M(atom='H 0 0 0; F 0 0 1')
>>> ci_scanner = ci.CISD(scf.RHF(mol)).as_scanner()
>>> e_tot = ci_scanner(gto.M(atom='H 0 0 0; F 0 0 1.1'))
>>> e_tot = ci_scanner(gto.M(atom='H 0 0 0; F 0 0 1.5'))
'''
from pyscf import gto
if isinstance(ci, lib.SinglePointScanner):
return ci
logger.info(ci, 'Set %s as a scanner', ci.__class__)
name = ci.__class__.__name__ + CISD_Scanner.__name_mixin__
return lib.set_class(CISD_Scanner(ci), (CISD_Scanner, ci.__class__), name)
[docs]
class CISD_Scanner(lib.SinglePointScanner):
def __init__(self, ci):
self.__dict__.update(ci.__dict__)
self._scf = ci._scf.as_scanner()
def __call__(self, mol_or_geom, ci0=None, **kwargs):
if isinstance(mol_or_geom, gto.Mole):
mol = mol_or_geom
else:
mol = self.mol.set_geom_(mol_or_geom, inplace=False)
self.reset(mol)
mf_scanner = self._scf
mf_scanner(mol)
self.mo_coeff = mf_scanner.mo_coeff
self.mo_occ = mf_scanner.mo_occ
if getattr(self.ci, 'size', 0) != self.vector_size():
self.ci = None
if ci0 is None:
# FIXME: Whether to use the initial guess from last step?
# If root flips, large errors may be found in the solutions
ci0 = self.ci
self.kernel(ci0, **kwargs)[0]
return self.e_tot
[docs]
class CISD(lib.StreamObject):
'''restricted CISD
Attributes:
verbose : int
Print level. Default value equals to :class:`Mole.verbose`
max_memory : float or int
Allowed memory in MB. Default value equals to
:class:`Mole.max_memory`
conv_tol : float
converge threshold. Default is 1e-9.
max_cycle : int
max number of iterations. Default is 50.
max_space : int
Davidson diagonalization space size. Default is 12.
direct : bool
AO-direct CISD. Default is False.
async_io : bool
Allow for asynchronous function execution. Default is True.
frozen : int or list
If integer is given, the inner-most orbitals are frozen from CI
amplitudes. Given the orbital indices (0-based) in a list, both
occupied and virtual orbitals can be frozen in CI calculation.
>>> mol = gto.M(atom = 'H 0 0 0; F 0 0 1.1', basis = 'ccpvdz')
>>> mf = scf.RHF(mol).run()
>>> # freeze 2 core orbitals
>>> myci = ci.CISD(mf).set(frozen = 2).run()
>>> # freeze 2 core orbitals and 3 high lying unoccupied orbitals
>>> myci.set(frozen = [0,1,16,17,18]).run()
Saved results
converged : bool
CISD converged or not
e_corr : float
CISD correlation correction
e_tot : float
Total CCSD energy (HF + correlation)
ci :
CI wavefunction coefficients
'''
conv_tol = getattr(__config__, 'ci_cisd_CISD_conv_tol', 1e-9)
max_cycle = getattr(__config__, 'ci_cisd_CISD_max_cycle', 50)
max_space = getattr(__config__, 'ci_cisd_CISD_max_space', 12)
lindep = getattr(__config__, 'ci_cisd_CISD_lindep', 1e-14)
# level shift in preconditioner is helpful to avoid singularity and linear
# dependence basis in davidson diagonalization solver
level_shift = getattr(__config__, 'ci_cisd_CISD_level_shift', 1e-3)
direct = getattr(__config__, 'ci_cisd_CISD_direct', False)
async_io = getattr(__config__, 'ci_cisd_CISD_async_io', True)
_keys = {
'conv_tol', 'max_cycle', 'max_space', 'lindep',
'level_shift', 'direct', 'async_io', 'mol', 'max_memory',
'nroots', 'frozen', 'chkfile', 'converged', 'mo_coeff', 'mo_occ',
'e_hf', 'e_corr', 'emp2', 'ci',
}
def __init__(self, mf, frozen=None, mo_coeff=None, mo_occ=None):
from pyscf.scf import hf
if isinstance(mf, hf.KohnShamDFT):
raise RuntimeError(
'CISD Warning: The first argument mf is a DFT object. '
'CISD calculation should be initialized with HF object.\n'
'DFT can be converted to HF object with the mf.to_hf() method\n')
if mo_coeff is None: mo_coeff = mf.mo_coeff
if mo_occ is None: mo_occ = mf.mo_occ
self.mol = mf.mol
self._scf = mf
self.verbose = self.mol.verbose
self.stdout = self.mol.stdout
self.max_memory = mf.max_memory
self.nroots = 1
self.frozen = frozen
self.chkfile = mf.chkfile
##################################################
# don't modify the following attributes, they are not input options
self.converged = False
self.mo_coeff = mo_coeff
self.mo_occ = mo_occ
self.e_hf = None
self.e_corr = None
self.emp2 = None
self.ci = None
self._nocc = None
self._nmo = None
[docs]
def dump_flags(self, verbose=None):
log = logger.new_logger(self, verbose)
log.info('')
log.info('******** %s ********', self.__class__)
log.info('CISD nocc = %s, nmo = %s', self.nocc, self.nmo)
if self.frozen is not None:
log.info('frozen orbitals %s', str(self.frozen))
log.info('max_cycle = %d', self.max_cycle)
log.info('direct = %d', self.direct)
log.info('conv_tol = %g', self.conv_tol)
log.info('max_cycle = %d', self.max_cycle)
log.info('max_space = %d', self.max_space)
log.info('lindep = %d', self.lindep)
log.info('nroots = %d', self.nroots)
log.info('max_memory %d MB (current use %d MB)',
self.max_memory, lib.current_memory()[0])
return self
@property
def e_tot(self):
return numpy.asarray(self.e_corr) + self.e_hf
@property
def nstates(self):
return self.nroots
@nstates.setter
def nstates(self, x):
self.nroots = x
@property
def nocc(self):
return self.get_nocc()
@nocc.setter
def nocc(self, n):
self._nocc = n
@property
def nmo(self):
return self.get_nmo()
@nmo.setter
def nmo(self, n):
self._nmo = n
[docs]
def vector_size(self):
'''The size of the vector which was returned from
:func:`amplitudes_to_cisdvec`
'''
nocc = self.nocc
nvir = self.nmo - nocc
return 1 + nocc*nvir + (nocc*nvir)**2
[docs]
def reset(self, mol=None):
if mol is not None:
self.mol = mol
self._scf.reset(mol)
return self
get_nocc = ccsd.get_nocc
get_nmo = ccsd.get_nmo
get_frozen_mask = ccsd.get_frozen_mask
get_e_hf = ccsd.get_e_hf
[docs]
def kernel(self, ci0=None, eris=None):
return self.cisd(ci0, eris)
[docs]
def cisd(self, ci0=None, eris=None):
self.e_hf = self.get_e_hf()
if eris is None:
eris = self.ao2mo(self.mo_coeff)
if self.verbose >= logger.WARN:
self.check_sanity()
self.dump_flags()
self.converged, self.e_corr, self.ci = \
kernel(self, eris, ci0, max_cycle=self.max_cycle,
tol=self.conv_tol, verbose=self.verbose)
self._finalize()
return self.e_corr, self.ci
def _finalize(self):
citype = self.__class__.__name__
if numpy.all(self.converged):
logger.info(self, '%s converged', citype)
else:
logger.info(self, '%s not converged', citype)
if self.nroots > 1:
for i,e in enumerate(self.e_tot):
logger.note(self, '%s root %d E = %.16g', citype, i, e)
else:
logger.note(self, 'E(%s) = %.16g E_corr = %.16g',
citype, self.e_tot, self.e_corr)
return self
[docs]
def get_init_guess(self, eris=None, nroots=1, diag=None):
'''
MP2 energy and MP2 initial guess(es) for CISD coefficients.
Kwargs:
eris : ccsd._ChemistsERIs (inheriting) object (poss diff for df)
Contains the various (pq|rs) integrals needed.
nroots : integer
Number of CISD solutions to be found.
diag : numpy array (1D)
e.g. CISD Hamiltonian diagonal in Slater determinant
space with HF energy subtracted.
Returns:
Tuple of float and numpy array or
tuple of float and list of numpy arrays (if nroots > 1)
MP2 energy and initial guess(es) for CISD coefficients.
'''
if eris is None: eris = self.ao2mo(self.mo_coeff)
nocc = self.nocc
mo_e = eris.mo_energy
e_ia = lib.direct_sum('i-a->ia', mo_e[:nocc], mo_e[nocc:])
ci0 = 1
ci1 = eris.fock[:nocc,nocc:] / e_ia
eris_ovvo = _cp(eris.ovvo)
ci2 = 2 * eris_ovvo.transpose(0,3,1,2)
ci2 -= eris_ovvo.transpose(0,3,2,1)
ci2 /= lib.direct_sum('ia,jb->ijab', e_ia, e_ia)
self.emp2 = numpy.einsum('ijab,iabj', ci2, eris_ovvo)
logger.info(self, 'Init t2, MP2 energy = %.15g', self.emp2)
if abs(self.emp2) < 1e-3 and abs(ci1).sum() < 1e-3:
# To avoid ci1 being stuck at local minimum
ci1 = 1e-1 / e_ia
ci_guess = amplitudes_to_cisdvec(ci0, ci1, ci2)
if nroots > 1:
civec_size = ci_guess.size
dtype = ci_guess.dtype
nroots = min(ci1.size+1, nroots) # Consider Koopmans' theorem only
if diag is None:
idx = range(1, nroots)
else:
idx = diag[:ci1.size+1].argsort()[1:nroots] # exclude HF determinant
ci_guess = [ci_guess]
for i in idx:
g = numpy.zeros(civec_size, dtype)
g[i] = 1.0
ci_guess.append(g)
return self.emp2, ci_guess
contract = contract
make_diagonal = make_diagonal
def _dot(self, x1, x2, nmo=None, nocc=None):
if nmo is None: nmo = self.nmo
if nocc is None: nocc = self.nocc
return dot(x1, x2, nmo, nocc)
[docs]
def ao2mo(self, mo_coeff=None):
nmo = self.nmo
nao = self.mo_coeff.shape[0]
nmo_pair = nmo * (nmo+1) // 2
nao_pair = nao * (nao+1) // 2
mem_incore = (max(nao_pair**2, nmo**4) + nmo_pair**2) * 8/1e6
mem_now = lib.current_memory()[0]
if (self._scf._eri is not None and
(mem_incore+mem_now < self.max_memory) or self.mol.incore_anyway):
return ccsd._make_eris_incore(self, mo_coeff)
if getattr(self._scf, 'with_df', None):
logger.warn(self, 'CISD detected DF being used in the HF object. '
'MO integrals are computed based on the DF 3-index tensors.\n'
'It\'s recommended to use dfccsd.CCSD for the '
'DF-CISD calculations')
return ccsd._make_df_eris_outcore(self, mo_coeff)
return ccsd._make_eris_outcore(self, mo_coeff)
def _add_vvvv(self, c2, eris, out=None, t2sym=None):
return ccsd._add_vvvv(self, None, c2, eris, out, False, t2sym)
[docs]
def to_fcivec(self, cisdvec, norb=None, nelec=None, frozen=None):
if norb is None: norb = self.nmo
if nelec is None: nelec = self.nocc*2
return to_fcivec(cisdvec, norb, nelec, frozen)
[docs]
def from_fcivec(self, fcivec, norb=None, nelec=None):
if norb is None: norb = self.nmo
if nelec is None: nelec = self.nocc*2
return from_fcivec(fcivec, norb, nelec)
make_rdm1 = make_rdm1
make_rdm2 = make_rdm2
trans_rdm1 = trans_rdm1
as_scanner = as_scanner
[docs]
def dump_chk(self, ci=None, frozen=None, mo_coeff=None, mo_occ=None):
if not self.chkfile:
return self
if ci is None: ci = self.ci
if frozen is None: frozen = self.frozen
# "None" cannot be serialized by the chkfile module
if frozen is None:
frozen = 0
ci_chk = {'e_corr': self.e_corr,
'ci': ci,
'frozen': frozen}
if mo_coeff is not None: ci_chk['mo_coeff'] = mo_coeff
if mo_occ is not None: ci_chk['mo_occ'] = mo_occ
if self._nmo is not None: ci_chk['_nmo'] = self._nmo
if self._nocc is not None: ci_chk['_nocc'] = self._nocc
lib.chkfile.save(self.chkfile, 'cisd', ci_chk)
[docs]
def amplitudes_to_cisdvec(self, c0, c1, c2):
return amplitudes_to_cisdvec(c0, c1, c2)
[docs]
def cisdvec_to_amplitudes(self, civec, nmo=None, nocc=None, copy=True):
if nmo is None: nmo = self.nmo
if nocc is None: nocc = self.nocc
return cisdvec_to_amplitudes(civec, nmo, nocc, copy=copy)
[docs]
def density_fit(self):
raise NotImplementedError
[docs]
def nuc_grad_method(self):
from pyscf.grad import cisd
return cisd.Gradients(self)
to_gpu = lib.to_gpu
[docs]
class RCISD(CISD):
pass
from pyscf import scf
scf.hf.RHF.CISD = lib.class_as_method(RCISD)
scf.rohf.ROHF.CISD = None
def _cp(a):
return numpy.asarray(a, order='C')
if __name__ == '__main__':
from pyscf import ao2mo
mol = gto.Mole()
mol.verbose = 0
mol.atom = [
['O', ( 0., 0. , 0. )],
['H', ( 0., -0.757, 0.587)],
['H', ( 0., 0.757 , 0.587)],]
mol.basis = 'sto3g'
mol.build()
mf = scf.RHF(mol).run()
myci = CISD(mf)
eris = ccsd._make_eris_outcore(myci, mf.mo_coeff)
ecisd, civec = myci.kernel(eris=eris)
print(ecisd - -0.048878084082066106)
nmo = myci.nmo
nocc = myci.nocc
rdm1 = myci.make_rdm1(civec)
rdm2 = myci.make_rdm2(civec)
h1e = reduce(numpy.dot, (mf.mo_coeff.T, mf.get_hcore(), mf.mo_coeff))
h2e = ao2mo.kernel(mf._eri, mf.mo_coeff)
h2e = ao2mo.restore(1, h2e, nmo)
e2 = (numpy.einsum('ij,ji', h1e, rdm1) +
numpy.einsum('ijkl,ijkl', h2e, rdm2) * .5)
print(ecisd + mf.e_tot - mol.energy_nuc() - e2) # = 0
print(abs(rdm1 - numpy.einsum('ijkk->ji', rdm2)/(mol.nelectron-1)).sum())
