# 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: Abdelrahman Ahmed <>
# Samragni Banerjee <samragnibanerjee4@gmail.com>
# James Serna <jamcar456@gmail.com>
# Terrence Stahl <>
# Alexander Sokolov <alexander.y.sokolov@gmail.com>
#
'''
Unrestricted algebraic diagrammatic construction
'''
import numpy as np
from pyscf import lib, symm
from pyscf.lib import logger
from pyscf.adc import uadc
from pyscf.adc import uadc_ao2mo
from pyscf.adc import radc_ao2mo
from pyscf.adc import dfadc
from pyscf import __config__
from pyscf import df
[docs]
def get_imds(adc, eris=None):
cput0 = (logger.process_clock(), logger.perf_counter())
log = logger.Logger(adc.stdout, adc.verbose)
if adc.method not in ("adc(2)", "adc(2)-x", "adc(3)"):
raise NotImplementedError(adc.method)
method = adc.method
t1 = adc.t1
t2 = adc.t2
nocc_a = adc.nocc_a
nocc_b = adc.nocc_b
nvir_a = adc.nvir_a
nvir_b = adc.nvir_b
ab_ind_a = np.tril_indices(nvir_a, k=-1)
ab_ind_b = np.tril_indices(nvir_b, k=-1)
e_vir_a = adc.mo_energy_a[nocc_a:]
e_vir_b = adc.mo_energy_b[nocc_b:]
idn_vir_a = np.identity(nvir_a)
idn_vir_b = np.identity(nvir_b)
if eris is None:
eris = adc.transform_integrals()
eris_ovvo = eris.ovvo
eris_OVVO = eris.OVVO
eris_ovVO = eris.ovVO
eris_OVvo = eris.OVvo
# a-b block
# Zeroth-order terms
M_ab_a = lib.einsum('ab,a->ab', idn_vir_a, e_vir_a)
M_ab_b = lib.einsum('ab,a->ab', idn_vir_b, e_vir_b)
# Second-order terms
t2_1_a = t2[0][0][:]
M_ab_a -= 0.5 * 0.5 * lib.einsum('lmad,lbdm->ab',t2_1_a, eris_ovvo,optimize=True)
M_ab_a += 0.5 * 0.5 * lib.einsum('lmad,ldbm->ab',t2_1_a, eris_ovvo,optimize=True)
M_ab_a -= 0.5 * 0.5 * lib.einsum('lmbd,ladm->ab',t2_1_a,eris_ovvo,optimize=True)
M_ab_a += 0.5 * 0.5 * lib.einsum('lmbd,ldam->ab',t2_1_a, eris_ovvo,optimize=True)
del t2_1_a
t2_1_b = t2[0][2][:]
M_ab_b -= 0.5 * 0.5 * lib.einsum('lmad,lbdm->ab',t2_1_b, eris_OVVO,optimize=True)
M_ab_b += 0.5 * 0.5 * lib.einsum('lmad,ldbm->ab',t2_1_b, eris_OVVO,optimize=True)
M_ab_b -= 0.5 * 0.5 * lib.einsum('lmbd,ladm->ab',t2_1_b, eris_OVVO,optimize=True)
M_ab_b += 0.5 * 0.5 * lib.einsum('lmbd,ldam->ab',t2_1_b, eris_OVVO,optimize=True)
del t2_1_b
t2_1_ab = t2[0][1][:]
M_ab_a -= 0.5 * lib.einsum('lmad,lbdm->ab',t2_1_ab, eris_ovVO,optimize=True)
M_ab_b -= 0.5 * lib.einsum('mlda,mdbl->ab',t2_1_ab, eris_ovVO,optimize=True)
M_ab_a -= 0.5 * lib.einsum('lmbd,ladm->ab',t2_1_ab, eris_ovVO,optimize=True)
M_ab_b -= 0.5 * lib.einsum('mldb,mdal->ab',t2_1_ab, eris_ovVO,optimize=True)
del t2_1_ab
cput0 = log.timer_debug1("Completed M_ab second-order terms ADC(2) calculation", *cput0)
#Third-order terms
if(method =='adc(3)'):
t1_2_a, t1_2_b = t1[0]
eris_oovv = eris.oovv
eris_OOVV = eris.OOVV
eris_OOvv = eris.OOvv
eris_ooVV = eris.ooVV
eris_ovvo = eris.ovvo
eris_OVVO = eris.OVVO
eris_OVvo = eris.OVvo
eris_ovVO = eris.ovVO
if isinstance(eris.ovvv, type(None)):
chnk_size = uadc_ao2mo.calculate_chunk_size(adc)
a = 0
for p in range(0,nocc_a,chnk_size):
eris_ovvv = dfadc.get_ovvv_spin_df(
adc, eris.Lov, eris.Lvv, p, chnk_size).reshape(-1,nvir_a,nvir_a,nvir_a)
k = eris_ovvv.shape[0]
M_ab_a += lib.einsum('ld,ldab->ab',t1_2_a[a:a+k], eris_ovvv,optimize=True)
M_ab_a -= lib.einsum('ld,lbad->ab',t1_2_a[a:a+k], eris_ovvv,optimize=True)
M_ab_a += lib.einsum('ld,ldab->ab',t1_2_a[a:a+k], eris_ovvv,optimize=True)
M_ab_a -= lib.einsum('ld,ladb->ab',t1_2_a[a:a+k], eris_ovvv,optimize=True)
del eris_ovvv
a += k
else :
eris_ovvv = radc_ao2mo.unpack_eri_1(eris.ovvv, nvir_a)
k = eris_ovvv.shape[0]
M_ab_a += lib.einsum('ld,ldab->ab',t1_2_a, eris_ovvv,optimize=True)
M_ab_a -= lib.einsum('ld,lbad->ab',t1_2_a, eris_ovvv,optimize=True)
M_ab_a += lib.einsum('ld,ldab->ab',t1_2_a, eris_ovvv,optimize=True)
M_ab_a -= lib.einsum('ld,ladb->ab',t1_2_a, eris_ovvv,optimize=True)
del eris_ovvv
if isinstance(eris.OVvv, type(None)):
chnk_size = uadc_ao2mo.calculate_chunk_size(adc)
a = 0
for p in range(0,nocc_b,chnk_size):
eris_OVvv = dfadc.get_ovvv_spin_df(
adc, eris.LOV, eris.Lvv, p, chnk_size).reshape(-1,nvir_b,nvir_a,nvir_a)
k = eris_OVvv.shape[0]
M_ab_a += lib.einsum('ld,ldab->ab',t1_2_b[a:a+k], eris_OVvv,optimize=True)
M_ab_a += lib.einsum('ld,ldab->ab',t1_2_b[a:a+k], eris_OVvv,optimize=True)
del eris_OVvv
a += k
else :
eris_OVvv = radc_ao2mo.unpack_eri_1(eris.OVvv, nvir_a)
M_ab_a += lib.einsum('ld,ldab->ab',t1_2_b, eris_OVvv,optimize=True)
M_ab_a += lib.einsum('ld,ldab->ab',t1_2_b, eris_OVvv,optimize=True)
del eris_OVvv
if isinstance(eris.OVVV, type(None)):
chnk_size = uadc_ao2mo.calculate_chunk_size(adc)
a = 0
for p in range(0,nocc_b,chnk_size):
eris_OVVV = dfadc.get_ovvv_spin_df(
adc, eris.LOV, eris.LVV, p, chnk_size).reshape(-1,nvir_b,nvir_b,nvir_b)
k = eris_OVVV.shape[0]
M_ab_b += lib.einsum('ld,ldab->ab',t1_2_b[a:a+k], eris_OVVV,optimize=True)
M_ab_b -= lib.einsum('ld,lbad->ab',t1_2_b[a:a+k], eris_OVVV,optimize=True)
M_ab_b += lib.einsum('ld,ldab->ab',t1_2_b[a:a+k], eris_OVVV,optimize=True)
M_ab_b -= lib.einsum('ld,ladb->ab',t1_2_b[a:a+k], eris_OVVV,optimize=True)
del eris_OVVV
a += k
else :
eris_OVVV = radc_ao2mo.unpack_eri_1(eris.OVVV, nvir_b)
M_ab_b += lib.einsum('ld,ldab->ab',t1_2_b, eris_OVVV,optimize=True)
M_ab_b -= lib.einsum('ld,lbad->ab',t1_2_b, eris_OVVV,optimize=True)
M_ab_b += lib.einsum('ld,ldab->ab',t1_2_b, eris_OVVV,optimize=True)
M_ab_b -= lib.einsum('ld,ladb->ab',t1_2_b, eris_OVVV,optimize=True)
del eris_OVVV
if isinstance(eris.ovVV, type(None)):
chnk_size = uadc_ao2mo.calculate_chunk_size(adc)
a = 0
for p in range(0,nocc_a,chnk_size):
eris_ovVV = dfadc.get_ovvv_spin_df(
adc, eris.Lov, eris.LVV, p, chnk_size).reshape(-1,nvir_a,nvir_b,nvir_b)
k = eris_ovVV.shape[0]
M_ab_b += lib.einsum('ld,ldab->ab',t1_2_a[a:a+k], eris_ovVV,optimize=True)
M_ab_b += lib.einsum('ld,ldab->ab',t1_2_a[a:a+k], eris_ovVV,optimize=True)
del eris_ovVV
a += k
else :
eris_ovVV = radc_ao2mo.unpack_eri_1(eris.ovVV, nvir_b)
k = eris_ovVV.shape[0]
M_ab_b += lib.einsum('ld,ldab->ab',t1_2_a, eris_ovVV,optimize=True)
M_ab_b += lib.einsum('ld,ldab->ab',t1_2_a, eris_ovVV,optimize=True)
del eris_ovVV
cput0 = log.timer_debug1("Completed M_ab ovvv ADC(3) calculation", *cput0)
t2_2_a = t2[1][0][:]
M_ab_a -= 0.5 * 0.5 * lib.einsum('lmad,lbdm->ab',t2_2_a, eris_ovvo,optimize=True)
M_ab_a += 0.5 * 0.5 * lib.einsum('lmad,ldbm->ab',t2_2_a, eris_ovvo,optimize=True)
M_ab_a -= 0.5 * 0.5 * lib.einsum('lmbd,ladm->ab',t2_2_a,eris_ovvo,optimize=True)
M_ab_a += 0.5 * 0.5 * lib.einsum('lmbd,ldam->ab',t2_2_a, eris_ovvo,optimize=True)
t2_2_b = t2[1][2][:]
M_ab_b -= 0.5 * 0.5 * lib.einsum('lmad,lbdm->ab',t2_2_b, eris_OVVO,optimize=True)
M_ab_b += 0.5 * 0.5 * lib.einsum('lmad,ldbm->ab',t2_2_b, eris_OVVO,optimize=True)
M_ab_b -= 0.5 * 0.5 * lib.einsum('lmbd,ladm->ab',t2_2_b, eris_OVVO,optimize=True)
M_ab_b += 0.5 * 0.5 * lib.einsum('lmbd,ldam->ab',t2_2_b, eris_OVVO,optimize=True)
t2_2_ab = t2[1][1][:]
M_ab_a -= 0.5 * lib.einsum('lmad,lbdm->ab',t2_2_ab, eris_ovVO,optimize=True)
M_ab_b -= 0.5 * lib.einsum('mlda,mdbl->ab',t2_2_ab, eris_ovVO,optimize=True)
M_ab_a -= 0.5 * lib.einsum('lmbd,ladm->ab',t2_2_ab, eris_ovVO,optimize=True)
M_ab_b -= 0.5 * lib.einsum('mldb,mdal->ab',t2_2_ab, eris_ovVO,optimize=True)
t2_1_a = t2[0][0][:]
t2_1_ab = t2[0][1][:]
M_ab_a -= 0.5 * lib.einsum('lnde,mlbd,neam->ab',t2_1_ab, t2_1_a, eris_OVvo, optimize=True)
M_ab_a += 0.5 * lib.einsum('lned,lmbd,nmae->ab',t2_1_ab, t2_1_ab, eris_OOvv, optimize=True)
M_ab_t = lib.einsum('lned,mlbd->nemb', t2_1_a,t2_1_a, optimize=True)
M_ab_a -= 0.5 * lib.einsum('nemb,nmae->ab',M_ab_t, eris_oovv, optimize=True)
M_ab_a += 0.5 * lib.einsum('nemb,maen->ab',M_ab_t, eris_ovvo, optimize=True)
M_ab_a -= 0.5 * lib.einsum('name,nmeb->ab',M_ab_t, eris_oovv, optimize=True)
M_ab_a += 0.5 * lib.einsum('name,nbem->ab',M_ab_t, eris_ovvo, optimize=True)
del M_ab_t
M_ab_t = lib.einsum('nled,mlbd->nemb', t2_1_ab,t2_1_ab, optimize=True)
M_ab_a += 0.5 * lib.einsum('nemb,nmae->ab',M_ab_t, eris_oovv, optimize=True)
M_ab_a -= 0.5 * lib.einsum('nemb,maen->ab',M_ab_t, eris_ovvo, optimize=True)
del M_ab_t
M_ab_t = lib.einsum('lnde,lmdb->nemb', t2_1_ab,t2_1_ab, optimize=True)
M_ab_b += 0.5 * lib.einsum('nemb,nmae->ab',M_ab_t, eris_OOVV, optimize=True)
M_ab_b -= 0.5 * lib.einsum('nemb,maen->ab',M_ab_t, eris_OVVO, optimize=True)
del M_ab_t
M_ab_b += 0.5 * lib.einsum('lned,lmdb,neam->ab',t2_1_a, t2_1_ab, eris_ovVO, optimize=True)
M_ab_b += 0.5 * lib.einsum('nlde,mldb,nmae->ab',t2_1_ab, t2_1_ab, eris_ooVV, optimize=True)
M_ab_a += 0.5 * lib.einsum('mled,nlad,nmeb->ab',t2_1_ab, t2_1_ab, eris_oovv, optimize=True)
M_ab_a -= 0.5 * lib.einsum('mled,nlad,nbem->ab',t2_1_ab, t2_1_ab, eris_ovvo, optimize=True)
M_ab_a += 0.5 * lib.einsum('lmed,lnad,nmeb->ab',t2_1_ab, t2_1_ab, eris_OOvv, optimize=True)
M_ab_a += 0.5 * lib.einsum('lmde,lnad,nbem->ab',t2_1_ab, t2_1_a, eris_ovVO, optimize=True)
M_ab_b += 0.5 * lib.einsum('lmde,lnda,nmeb->ab',t2_1_ab, t2_1_ab, eris_OOVV, optimize=True)
M_ab_b -= 0.5 * lib.einsum('lmde,lnda,nbem->ab',t2_1_ab, t2_1_ab, eris_OVVO, optimize=True)
M_ab_b += 0.5 * lib.einsum('mlde,nlda,nmeb->ab',t2_1_ab, t2_1_ab, eris_ooVV, optimize=True)
M_ab_b -= 0.5 * lib.einsum('mled,lnda,nbem->ab',t2_1_a, t2_1_ab, eris_OVvo, optimize=True)
M_ab_a += 0.5*lib.einsum('lned,mled,nmab->ab',t2_1_a, t2_1_a, eris_oovv, optimize=True)
M_ab_a -= 0.5*lib.einsum('lned,mled,nbam->ab',t2_1_a, t2_1_a, eris_ovvo, optimize=True)
M_ab_a -= lib.einsum('nled,mled,nmab->ab',t2_1_ab, t2_1_ab, eris_oovv, optimize=True)
M_ab_a += lib.einsum('nled,mled,nbam->ab',t2_1_ab, t2_1_ab, eris_ovvo, optimize=True)
M_ab_a -= lib.einsum('lned,lmed,nmab->ab',t2_1_ab, t2_1_ab, eris_OOvv, optimize=True)
M_ab_b -= lib.einsum('lned,lmed,nmab->ab',t2_1_ab, t2_1_ab, eris_OOVV, optimize=True)
M_ab_b += lib.einsum('lned,lmed,nbam->ab',t2_1_ab, t2_1_ab, eris_OVVO, optimize=True)
M_ab_b += 0.5*lib.einsum('lned,mled,nmab->ab',t2_1_a, t2_1_a, eris_ooVV, optimize=True)
M_ab_b -= lib.einsum('nled,mled,nmab->ab',t2_1_ab, t2_1_ab, eris_ooVV, optimize=True)
t2_1_b = t2[0][2][:]
M_ab_a += 0.5 * lib.einsum('lned,mlbd,neam->ab',t2_1_b, t2_1_ab, eris_OVvo, optimize=True)
M_ab_t = lib.einsum('lned,mlbd->nemb', t2_1_b,t2_1_b, optimize=True)
M_ab_b -= 0.5 * lib.einsum('nemb,nmae->ab',M_ab_t, eris_OOVV, optimize=True)
M_ab_b += 0.5 * lib.einsum('nemb,maen->ab',M_ab_t, eris_OVVO, optimize=True)
M_ab_b -= 0.5 * lib.einsum('name,nmeb->ab',M_ab_t, eris_OOVV, optimize=True)
M_ab_b += 0.5 * lib.einsum('name,nbem->ab',M_ab_t, eris_OVVO, optimize=True)
del M_ab_t
M_ab_b -= 0.5 * lib.einsum('nled,mlbd,neam->ab',t2_1_ab, t2_1_b, eris_ovVO, optimize=True)
M_ab_a -= 0.5 * lib.einsum('mled,nlad,nbem->ab',t2_1_b, t2_1_ab, eris_ovVO, optimize=True)
M_ab_b += 0.5 * lib.einsum('mled,lnad,nbem->ab',t2_1_ab, t2_1_b, eris_OVvo, optimize=True)
M_ab_a += 0.5 * lib.einsum('lned,mled,nmab->ab',t2_1_b, t2_1_b, eris_OOvv, optimize=True)
M_ab_b += 0.5 * lib.einsum('lned,mled,nmab->ab',t2_1_b, t2_1_b, eris_OOVV, optimize=True)
M_ab_b -= 0.5 * lib.einsum('lned,mled,nbam->ab',t2_1_b, t2_1_b, eris_OVVO, optimize=True)
log.timer_debug1("Completed M_ab ADC(3) small integrals calculation")
t2_1_a = t2[0][0][:]
t2_1_ab = t2[0][1][:]
if isinstance(eris.vvvv_p,np.ndarray):
eris_vvvv = radc_ao2mo.unpack_eri_2(eris.vvvv_p, nvir_a)
M_ab_a -= 0.5 * 0.25*lib.einsum('mlef,mlbd,adef->ab',
t2_1_a, t2_1_a, eris_vvvv, optimize=True)
M_ab_a -= 0.5*lib.einsum('mldf,mled,aebf->ab',t2_1_a, t2_1_a, eris_vvvv, optimize=True)
M_ab_a += lib.einsum('mlfd,mled,aebf->ab',t2_1_ab, t2_1_ab, eris_vvvv, optimize=True)
del eris_vvvv
M_ab_a -= 2 * 0.5 * 0.25*lib.einsum('mlaf,mlbf->ab',
t2_1_a, adc.imds.t2_1_vvvv[0], optimize=True)
else:
M_ab_a -= 2*0.5*0.25*lib.einsum('mlad,mlbd->ab',
adc.imds.t2_1_vvvv[0], t2_1_a, optimize=True)
M_ab_a -= 2*0.5*0.25*lib.einsum('mlaf,mlbf->ab', t2_1_a,
adc.imds.t2_1_vvvv[0], optimize=True)
if isinstance(eris.vvvv_p, list):
a = 0
temp = np.zeros((nvir_a,nvir_a))
for dataset in eris.vvvv_p:
k = dataset.shape[0]
vvvv = dataset[:]
eris_vvvv = np.zeros((k,nvir_a,nvir_a,nvir_a))
eris_vvvv[:,:,ab_ind_a[0],ab_ind_a[1]] = vvvv
eris_vvvv[:,:,ab_ind_a[1],ab_ind_a[0]] = -vvvv
temp[a:a+k] -= 0.5*lib.einsum('mldf,mled,aebf->ab',
t2_1_a, t2_1_a, eris_vvvv, optimize=True)
temp[a:a+k] += lib.einsum('mlfd,mled,aebf->ab',t2_1_ab,
t2_1_ab, eris_vvvv, optimize=True)
del eris_vvvv
a += k
M_ab_a += temp
a = 0
temp = np.zeros((nvir_b,nvir_b))
for dataset in eris.VvVv_p:
k = dataset.shape[0]
eris_VvVv = dataset[:].reshape(-1,nvir_a,nvir_b,nvir_a)
temp[a:a+k] -= 0.5*lib.einsum('mldf,mled,aebf->ab',
t2_1_a, t2_1_a, eris_VvVv, optimize=True)
temp[a:a+k] += lib.einsum('mlfd,mled,aebf->ab',t2_1_ab,
t2_1_ab, eris_VvVv, optimize=True)
a += k
M_ab_b += temp
elif isinstance(eris.vvvv_p, type(None)):
a = 0
temp = np.zeros((nvir_a,nvir_a))
chnk_size = uadc_ao2mo.calculate_chunk_size(adc)
for p in range(0,nvir_a,chnk_size):
vvvv = dfadc.get_vvvv_antisym_df(adc, eris.Lvv, p, chnk_size)
k = vvvv.shape[0]
eris_vvvv = np.zeros((k,nvir_a,nvir_a,nvir_a))
eris_vvvv[:,:,ab_ind_a[0],ab_ind_a[1]] = vvvv
eris_vvvv[:,:,ab_ind_a[1],ab_ind_a[0]] = -vvvv
temp[a:a+k] -= 0.5*lib.einsum('mldf,mled,aebf->ab',
t2_1_a, t2_1_a, eris_vvvv, optimize=True)
temp[a:a+k] += lib.einsum('mlfd,mled,aebf->ab',t2_1_ab,
t2_1_ab, eris_vvvv, optimize=True)
del eris_vvvv
a += k
M_ab_a += temp
del temp
a = 0
temp = np.zeros((nvir_b,nvir_b))
for p in range(0,nvir_b,chnk_size):
eris_VvVv = dfadc.get_vVvV_df(adc, eris.LVV, eris.Lvv, p, chnk_size)
k = eris_VvVv.shape[0]
temp[a:a+k] -= 0.5*lib.einsum('mldf,mled,aebf->ab',
t2_1_a, t2_1_a, eris_VvVv, optimize=True)
temp[a:a+k] += lib.einsum('mlfd,mled,aebf->ab',t2_1_ab,
t2_1_ab, eris_VvVv, optimize=True)
a += k
M_ab_b += temp
del temp
t2_1_b = t2[0][2][:]
if isinstance(eris.vVvV_p,np.ndarray):
eris_vVvV = eris.vVvV_p
eris_vVvV = eris_vVvV.reshape(nvir_a,nvir_b,nvir_a,nvir_b)
M_ab_a -= 0.5*lib.einsum('mlef,mlbd,adef->ab',t2_1_ab,
t2_1_ab, eris_vVvV, optimize=True)
M_ab_a -= 0.5*lib.einsum('mldf,mled,aebf->ab',t2_1_b, t2_1_b, eris_vVvV, optimize=True)
M_ab_a += lib.einsum('mldf,mlde,aebf->ab',t2_1_ab, t2_1_ab, eris_vVvV, optimize=True)
M_ab_b -= 0.5*lib.einsum('mlef,mldb,daef->ab',t2_1_ab,
t2_1_ab, eris_vVvV, optimize=True)
M_ab_b -= 0.5*lib.einsum('mldf,mled,eafb->ab',t2_1_a, t2_1_a, eris_vVvV, optimize=True)
M_ab_b += lib.einsum('mlfd,mled,eafb->ab',t2_1_ab, t2_1_ab, eris_vVvV, optimize=True)
eris_vVvV = eris_vVvV.reshape(nvir_a*nvir_b,nvir_a*nvir_b)
M_ab_a -= 0.5*lib.einsum('mlaf,mlbf->ab',t2_1_ab, adc.imds.t2_1_vvvv[1], optimize=True)
M_ab_b -= 0.5*lib.einsum('mlfa,mlfb->ab',t2_1_ab, adc.imds.t2_1_vvvv[1], optimize=True)
else:
M_ab_a -= 0.5 * lib.einsum('mlad,mlbd->ab',
adc.imds.t2_1_vvvv[1], t2_1_ab, optimize=True)
M_ab_b -= 0.5 * lib.einsum('mlda,mldb->ab',
adc.imds.t2_1_vvvv[1], t2_1_ab, optimize=True)
M_ab_a -= 0.5 * lib.einsum('mlaf,mlbf->ab', t2_1_ab,
adc.imds.t2_1_vvvv[1], optimize=True)
M_ab_b -= 0.5 * lib.einsum('mlfa,mlfb->ab', t2_1_ab,
adc.imds.t2_1_vvvv[1], optimize=True)
del t2_1_a
if isinstance(eris.VVVV_p,np.ndarray):
eris_VVVV = radc_ao2mo.unpack_eri_2(eris.VVVV_p, nvir_b)
M_ab_b -= 0.5*0.25*lib.einsum('mlef,mlbd,adef->ab',t2_1_b,
t2_1_b, eris_VVVV, optimize=True)
M_ab_b -= 0.5*lib.einsum('mldf,mled,aebf->ab',t2_1_b, t2_1_b, eris_VVVV, optimize=True)
M_ab_b += lib.einsum('mldf,mlde,aebf->ab',t2_1_ab, t2_1_ab, eris_VVVV, optimize=True)
del eris_VVVV
M_ab_b -= 2 * 0.5 * 0.25*lib.einsum('mlaf,mlbf->ab',
t2_1_b, adc.imds.t2_1_vvvv[2], optimize=True)
else:
M_ab_b -= 2 * 0.5 * 0.25*lib.einsum('mlad,mlbd->ab',
adc.imds.t2_1_vvvv[2], t2_1_b, optimize=True)
M_ab_b -= 2 * 0.5 * 0.25*lib.einsum('mlaf,mlbf->ab',
t2_1_b, adc.imds.t2_1_vvvv[2], optimize=True)
if isinstance(eris.vvvv_p, list):
a = 0
temp = np.zeros((nvir_b,nvir_b))
for dataset in eris.VVVV_p:
k = dataset.shape[0]
VVVV = dataset[:]
eris_VVVV = np.zeros((k,nvir_b,nvir_b,nvir_b))
eris_VVVV[:,:,ab_ind_b[0],ab_ind_b[1]] = VVVV
eris_VVVV[:,:,ab_ind_b[1],ab_ind_b[0]] = -VVVV
temp[a:a+k] -= 0.5*lib.einsum('mldf,mled,aebf->ab',
t2_1_b, t2_1_b, eris_VVVV, optimize=True)
temp[a:a+k] += lib.einsum('mldf,mlde,aebf->ab',t2_1_ab,
t2_1_ab, eris_VVVV, optimize=True)
del eris_VVVV
a += k
M_ab_b += temp
a = 0
temp = np.zeros((nvir_a,nvir_a))
for dataset in eris.vVvV_p:
k = dataset.shape[0]
eris_vVvV = dataset[:].reshape(-1,nvir_b,nvir_a,nvir_b)
temp[a:a+k] -= 0.5*lib.einsum('mldf,mled,aebf->ab',
t2_1_b, t2_1_b, eris_vVvV, optimize=True)
temp[a:a+k] += lib.einsum('mldf,mlde,aebf->ab',t2_1_ab,
t2_1_ab, eris_vVvV, optimize=True)
a += k
M_ab_a += temp
elif isinstance(eris.vvvv_p, type(None)):
a = 0
temp = np.zeros((nvir_b,nvir_b))
chnk_size = uadc_ao2mo.calculate_chunk_size(adc)
for p in range(0,nvir_b,chnk_size):
VVVV = dfadc.get_vvvv_antisym_df(adc, eris.LVV, p, chnk_size)
k = VVVV.shape[0]
eris_VVVV = np.zeros((k,nvir_b,nvir_b,nvir_b))
eris_VVVV[:,:,ab_ind_b[0],ab_ind_b[1]] = VVVV
eris_VVVV[:,:,ab_ind_b[1],ab_ind_b[0]] = -VVVV
temp[a:a+k] -= 0.5*lib.einsum('mldf,mled,aebf->ab',
t2_1_b, t2_1_b, eris_VVVV, optimize=True)
temp[a:a+k] += lib.einsum('mldf,mlde,aebf->ab',t2_1_ab,
t2_1_ab, eris_VVVV, optimize=True)
del eris_VVVV
a += k
M_ab_b += temp
del temp
a = 0
temp = np.zeros((nvir_a,nvir_a))
chnk_size = uadc_ao2mo.calculate_chunk_size(adc)
for p in range(0,nvir_a,chnk_size):
eris_vVvV = dfadc.get_vVvV_df(adc, eris.Lvv, eris.LVV, p, chnk_size)
k = eris_vVvV.shape[0]
temp[a:a+k] -= 0.5*lib.einsum('mldf,mled,aebf->ab',
t2_1_b, t2_1_b, eris_vVvV, optimize=True)
temp[a:a+k] += lib.einsum('mldf,mlde,aebf->ab',t2_1_ab,
t2_1_ab, eris_vVvV, optimize=True)
a += k
M_ab_a += temp
del temp
del t2_1_ab, t2_1_b
M_ab = (M_ab_a, M_ab_b)
cput0 = log.timer_debug1("Completed M_ab ADC(3) calculation", *cput0)
return M_ab
[docs]
def get_diag(adc,M_ab=None,eris=None):
if adc.method not in ("adc(2)", "adc(2)-x", "adc(3)"):
raise NotImplementedError(adc.method)
if M_ab is None:
M_ab = adc.get_imds()
M_ab_a, M_ab_b = M_ab[0], M_ab[1]
nocc_a = adc.nocc_a
nocc_b = adc.nocc_b
nvir_a = adc.nvir_a
nvir_b = adc.nvir_b
n_singles_a = nvir_a
n_singles_b = nvir_b
n_doubles_aaa = nvir_a * (nvir_a - 1) * nocc_a // 2
n_doubles_bab = nocc_b * nvir_a * nvir_b
n_doubles_aba = nocc_a * nvir_b * nvir_a
n_doubles_bbb = nvir_b * (nvir_b - 1) * nocc_b // 2
dim = n_singles_a + n_singles_b + n_doubles_aaa + n_doubles_bab + n_doubles_aba + n_doubles_bbb
e_occ_a = adc.mo_energy_a[:nocc_a]
e_occ_b = adc.mo_energy_b[:nocc_b]
e_vir_a = adc.mo_energy_a[nocc_a:]
e_vir_b = adc.mo_energy_b[nocc_b:]
ab_ind_a = np.tril_indices(nvir_a, k=-1)
ab_ind_b = np.tril_indices(nvir_b, k=-1)
s_a = 0
f_a = n_singles_a
s_b = f_a
f_b = s_b + n_singles_b
s_aaa = f_b
f_aaa = s_aaa + n_doubles_aaa
s_bab = f_aaa
f_bab = s_bab + n_doubles_bab
s_aba = f_bab
f_aba = s_aba + n_doubles_aba
s_bbb = f_aba
f_bbb = s_bbb + n_doubles_bbb
d_i_a = e_occ_a[:,None]
d_ab_a = e_vir_a[:,None] + e_vir_a
D_n_a = -d_i_a + d_ab_a.reshape(-1)
D_n_a = D_n_a.reshape((nocc_a,nvir_a,nvir_a))
D_iab_a = D_n_a.copy()[:,ab_ind_a[0],ab_ind_a[1]].reshape(-1)
d_i_b = e_occ_b[:,None]
d_ab_b = e_vir_b[:,None] + e_vir_b
D_n_b = -d_i_b + d_ab_b.reshape(-1)
D_n_b = D_n_b.reshape((nocc_b,nvir_b,nvir_b))
D_iab_b = D_n_b.copy()[:,ab_ind_b[0],ab_ind_b[1]].reshape(-1)
d_ab_ab = e_vir_a[:,None] + e_vir_b
d_i_b = e_occ_b[:,None]
D_n_bab = -d_i_b + d_ab_ab.reshape(-1)
D_iab_bab = D_n_bab.reshape(-1)
d_ab_ab = e_vir_b[:,None] + e_vir_a
d_i_a = e_occ_a[:,None]
D_n_aba = -d_i_a + d_ab_ab.reshape(-1)
D_iab_aba = D_n_aba.reshape(-1)
diag = np.zeros(dim)
# Compute precond in p1-p1 block
M_ab_a_diag = np.diagonal(M_ab_a)
M_ab_b_diag = np.diagonal(M_ab_b)
diag[s_a:f_a] = M_ab_a_diag.copy()
diag[s_b:f_b] = M_ab_b_diag.copy()
# Compute precond in 2p1h-2p1h block
diag[s_aaa:f_aaa] = D_iab_a
diag[s_bab:f_bab] = D_iab_bab
diag[s_aba:f_aba] = D_iab_aba
diag[s_bbb:f_bbb] = D_iab_b
# ###### Additional terms for the preconditioner ####
# if (method == "adc(2)-x" or method == "adc(3)"):
#
# if eris is None:
# eris = adc.transform_integrals()
#
# if isinstance(eris.vvvv_p, np.ndarray):
#
# eris_oovv = eris.oovv
# eris_ovvo = eris.ovvo
# eris_OOVV = eris.OOVV
# eris_OVVO = eris.OVVO
# eris_OOvv = eris.OOvv
# eris_ooVV = eris.ooVV
#
# eris_vvvv = eris.vvvv_p
# temp = np.zeros((nocc_a, eris_vvvv.shape[0]))
# temp[:] += np.diag(eris_vvvv)
# diag[s_aaa:f_aaa] += temp.reshape(-1)
#
# eris_VVVV = eris.VVVV_p
# temp = np.zeros((nocc_b, eris_VVVV.shape[0]))
# temp[:] += np.diag(eris_VVVV)
# diag[s_bbb:f_bbb] += temp.reshape(-1)
#
# eris_vVvV = eris.vVvV_p
# temp = np.zeros((nocc_b, eris_vVvV.shape[0]))
# temp[:] += np.diag(eris_vVvV)
# diag[s_bab:f_bab] += temp.reshape(-1)
#
# temp = np.zeros((nocc_a, nvir_a, nvir_b))
# temp[:] += np.diag(eris_vVvV).reshape(nvir_a,nvir_b)
# temp = np.ascontiguousarray(temp.transpose(0,2,1))
# diag[s_aba:f_aba] += temp.reshape(-1)
#
# eris_ovov_p = np.ascontiguousarray(eris_oovv.transpose(0,2,1,3))
# eris_ovov_p -= np.ascontiguousarray(eris_ovvo.transpose(0,2,3,1))
# eris_ovov_p = eris_ovov_p.reshape(nocc_a*nvir_a, nocc_a*nvir_a)
#
# temp = np.zeros((eris_ovov_p.shape[0],nvir_a))
# temp.T[:] += np.diagonal(eris_ovov_p)
# temp = temp.reshape(nocc_a, nvir_a, nvir_a)
# diag[s_aaa:f_aaa] += -temp[:,ab_ind_a[0],ab_ind_a[1]].reshape(-1)
#
# temp = np.ascontiguousarray(temp.transpose(0,2,1))
# diag[s_aaa:f_aaa] += -temp[:,ab_ind_a[0],ab_ind_a[1]].reshape(-1)
#
# eris_OVOV_p = np.ascontiguousarray(eris_OOVV.transpose(0,2,1,3))
# eris_OVOV_p -= np.ascontiguousarray(eris_OVVO.transpose(0,2,3,1))
# eris_OVOV_p = eris_OVOV_p.reshape(nocc_b*nvir_b, nocc_b*nvir_b)
#
# temp = np.zeros((eris_OVOV_p.shape[0],nvir_b))
# temp.T[:] += np.diagonal(eris_OVOV_p)
# temp = temp.reshape(nocc_b, nvir_b, nvir_b)
# diag[s_bbb:f_bbb] += -temp[:,ab_ind_b[0],ab_ind_b[1]].reshape(-1)
#
# temp = np.ascontiguousarray(temp.transpose(0,2,1))
# diag[s_bbb:f_bbb] += -temp[:,ab_ind_b[0],ab_ind_b[1]].reshape(-1)
#
# temp = np.zeros((nvir_a, nocc_b, nvir_b))
# temp[:] += np.diagonal(eris_OVOV_p).reshape(nocc_b, nvir_b)
# temp = np.ascontiguousarray(temp.transpose(1,0,2))
# diag[s_bab:f_bab] += -temp.reshape(-1)
#
# temp = np.zeros((nvir_b, nocc_a, nvir_a))
# temp[:] += np.diagonal(eris_ovov_p).reshape(nocc_a, nvir_a)
# temp = np.ascontiguousarray(temp.transpose(1,0,2))
# diag[s_aba:f_aba] += -temp.reshape(-1)
#
# eris_OvOv_p = np.ascontiguousarray(eris_OOvv.transpose(0,2,1,3))
# eris_OvOv_p = eris_OvOv_p.reshape(nocc_b*nvir_a, nocc_b*nvir_a)
#
# temp = np.zeros((nvir_b, nocc_b, nvir_a))
# temp[:] += np.diagonal(eris_OvOv_p).reshape(nocc_b,nvir_a)
# temp = np.ascontiguousarray(temp.transpose(1,2,0))
# diag[s_bab:f_bab] += -temp.reshape(-1)
#
# eris_oVoV_p = np.ascontiguousarray(eris_ooVV.transpose(0,2,1,3))
# eris_oVoV_p = eris_oVoV_p.reshape(nocc_a*nvir_b, nocc_a*nvir_b)
#
# temp = np.zeros((nvir_a, nocc_a, nvir_b))
# temp[:] += np.diagonal(eris_oVoV_p).reshape(nocc_a,nvir_b)
# temp = np.ascontiguousarray(temp.transpose(1,2,0))
# diag[s_aba:f_aba] += -temp.reshape(-1)
# else:
# raise Exception("Precond not available for out-of-core and density-fitted algo")
return diag
[docs]
def matvec(adc, M_ab=None, eris=None):
if adc.method not in ("adc(2)", "adc(2)-x", "adc(3)"):
raise NotImplementedError(adc.method)
method = adc.method
nocc_a = adc.nocc_a
nocc_b = adc.nocc_b
nvir_a = adc.nvir_a
nvir_b = adc.nvir_b
ab_ind_a = np.tril_indices(nvir_a, k=-1)
ab_ind_b = np.tril_indices(nvir_b, k=-1)
n_singles_a = nvir_a
n_singles_b = nvir_b
n_doubles_aaa = nvir_a * (nvir_a - 1) * nocc_a // 2
n_doubles_bab = nocc_b * nvir_a * nvir_b
n_doubles_aba = nocc_a * nvir_b * nvir_a
n_doubles_bbb = nvir_b * (nvir_b - 1) * nocc_b // 2
dim = n_singles_a + n_singles_b + n_doubles_aaa + n_doubles_bab + n_doubles_aba + n_doubles_bbb
e_occ_a = adc.mo_energy_a[:nocc_a]
e_occ_b = adc.mo_energy_b[:nocc_b]
e_vir_a = adc.mo_energy_a[nocc_a:]
e_vir_b = adc.mo_energy_b[nocc_b:]
if eris is None:
eris = adc.transform_integrals()
d_i_a = e_occ_a[:,None]
d_ab_a = e_vir_a[:,None] + e_vir_a
D_n_a = -d_i_a + d_ab_a.reshape(-1)
D_n_a = D_n_a.reshape((nocc_a,nvir_a,nvir_a))
D_iab_a = D_n_a.copy()[:,ab_ind_a[0],ab_ind_a[1]].reshape(-1)
d_i_b = e_occ_b[:,None]
d_ab_b = e_vir_b[:,None] + e_vir_b
D_n_b = -d_i_b + d_ab_b.reshape(-1)
D_n_b = D_n_b.reshape((nocc_b,nvir_b,nvir_b))
D_iab_b = D_n_b.copy()[:,ab_ind_b[0],ab_ind_b[1]].reshape(-1)
d_ab_ab = e_vir_a[:,None] + e_vir_b
d_i_b = e_occ_b[:,None]
D_n_bab = -d_i_b + d_ab_ab.reshape(-1)
D_iab_bab = D_n_bab.reshape(-1)
d_ab_ab = e_vir_b[:,None] + e_vir_a
d_i_a = e_occ_a[:,None]
D_n_aba = -d_i_a + d_ab_ab.reshape(-1)
D_iab_aba = D_n_aba.reshape(-1)
s_a = 0
f_a = n_singles_a
s_b = f_a
f_b = s_b + n_singles_b
s_aaa = f_b
f_aaa = s_aaa + n_doubles_aaa
s_bab = f_aaa
f_bab = s_bab + n_doubles_bab
s_aba = f_bab
f_aba = s_aba + n_doubles_aba
s_bbb = f_aba
f_bbb = s_bbb + n_doubles_bbb
if M_ab is None:
M_ab = adc.get_imds()
M_ab_a, M_ab_b = M_ab
#Calculate sigma vector
def sigma_(r):
cput0 = (logger.process_clock(), logger.perf_counter())
log = logger.Logger(adc.stdout, adc.verbose)
s = np.zeros((dim))
r_a = r[s_a:f_a]
r_b = r[s_b:f_b]
r_aaa = r[s_aaa:f_aaa]
r_bab = r[s_bab:f_bab]
r_aba = r[s_aba:f_aba]
r_bbb = r[s_bbb:f_bbb]
r_aaa_ = np.zeros((nocc_a, nvir_a, nvir_a))
r_aaa_[:, ab_ind_a[0], ab_ind_a[1]] = r_aaa.reshape(nocc_a, -1)
r_aaa_[:, ab_ind_a[1], ab_ind_a[0]] = -r_aaa.reshape(nocc_a, -1)
r_bbb_ = np.zeros((nocc_b, nvir_b, nvir_b))
r_bbb_[:, ab_ind_b[0], ab_ind_b[1]] = r_bbb.reshape(nocc_b, -1)
r_bbb_[:, ab_ind_b[1], ab_ind_b[0]] = -r_bbb.reshape(nocc_b, -1)
r_aba = r_aba.reshape(nocc_a,nvir_b,nvir_a)
r_bab = r_bab.reshape(nocc_b,nvir_a,nvir_b)
############ ADC(2) ab block ############################
s[s_a:f_a] = lib.einsum('ab,b->a',M_ab_a,r_a)
s[s_b:f_b] = lib.einsum('ab,b->a',M_ab_b,r_b)
############ ADC(2) a - ibc and ibc - a coupling blocks #########################
temp = np.zeros((nocc_a, nvir_a, nvir_a))
if isinstance(eris.ovvv, type(None)):
chnk_size = uadc_ao2mo.calculate_chunk_size(adc)
a = 0
for p in range(0,nocc_a,chnk_size):
eris_ovvv = dfadc.get_ovvv_spin_df(
adc, eris.Lov, eris.Lvv, p, chnk_size).reshape(-1,nvir_a,nvir_a,nvir_a)
k = eris_ovvv.shape[0]
s[s_a:f_a] += 0.5*lib.einsum('icab,ibc->a',eris_ovvv, r_aaa_[a:a+k], optimize=True)
s[s_a:f_a] -= 0.5*lib.einsum('ibac,ibc->a',eris_ovvv, r_aaa_[a:a+k], optimize=True)
temp[a:a+k] += lib.einsum('icab,a->ibc', eris_ovvv, r_a, optimize=True)
temp[a:a+k] -= lib.einsum('ibac,a->ibc', eris_ovvv, r_a, optimize=True)
del eris_ovvv
a += k
else :
eris_ovvv = radc_ao2mo.unpack_eri_1(eris.ovvv, nvir_a)
s[s_a:f_a] += 0.5*lib.einsum('icab,ibc->a',eris_ovvv, r_aaa_, optimize=True)
s[s_a:f_a] -= 0.5*lib.einsum('ibac,ibc->a',eris_ovvv, r_aaa_, optimize=True)
temp += lib.einsum('icab,a->ibc', eris_ovvv, r_a, optimize=True)
temp -= lib.einsum('ibac,a->ibc', eris_ovvv, r_a, optimize=True)
del eris_ovvv
s[s_aaa:f_aaa] += temp[:,ab_ind_a[0],ab_ind_a[1]].reshape(-1)
del temp
temp = np.zeros((nocc_b, nvir_a, nvir_b))
if isinstance(eris.OVvv, type(None)):
chnk_size = uadc_ao2mo.calculate_chunk_size(adc)
a = 0
for p in range(0,nocc_b,chnk_size):
eris_OVvv = dfadc.get_ovvv_spin_df(
adc, eris.LOV, eris.Lvv, p, chnk_size).reshape(-1,nvir_b,nvir_a,nvir_a)
k = eris_OVvv.shape[0]
s[s_a:f_a] += lib.einsum('icab,ibc->a', eris_OVvv, r_bab[a:a+k], optimize=True)
temp[a:a+k] += lib.einsum('icab,a->ibc', eris_OVvv, r_a, optimize=True)
del eris_OVvv
a += k
else :
eris_OVvv = radc_ao2mo.unpack_eri_1(eris.OVvv, nvir_a)
s[s_a:f_a] += lib.einsum('icab,ibc->a', eris_OVvv, r_bab, optimize=True)
temp += lib.einsum('icab,a->ibc', eris_OVvv, r_a, optimize=True)
del eris_OVvv
s[s_bab:f_bab] += temp.reshape(-1)
del temp
temp = np.zeros((nocc_b, nvir_b, nvir_b))
if isinstance(eris.OVVV, type(None)):
chnk_size = uadc_ao2mo.calculate_chunk_size(adc)
a = 0
for p in range(0,nocc_b,chnk_size):
eris_OVVV = dfadc.get_ovvv_spin_df(
adc, eris.LOV, eris.LVV, p, chnk_size).reshape(-1,nvir_b,nvir_b,nvir_b)
k = eris_OVVV.shape[0]
s[s_b:f_b] += 0.5*lib.einsum('icab,ibc->a',eris_OVVV, r_bbb_[a:a+k], optimize=True)
s[s_b:f_b] -= 0.5*lib.einsum('ibac,ibc->a',eris_OVVV, r_bbb_[a:a+k], optimize=True)
temp[a:a+k] += lib.einsum('icab,a->ibc', eris_OVVV, r_b, optimize=True)
temp[a:a+k] -= lib.einsum('ibac,a->ibc', eris_OVVV, r_b, optimize=True)
del eris_OVVV
a += k
else :
eris_OVVV = radc_ao2mo.unpack_eri_1(eris.OVVV, nvir_b)
s[s_b:f_b] += 0.5*lib.einsum('icab,ibc->a',eris_OVVV, r_bbb_, optimize=True)
s[s_b:f_b] -= 0.5*lib.einsum('ibac,ibc->a',eris_OVVV, r_bbb_, optimize=True)
temp += lib.einsum('icab,a->ibc', eris_OVVV, r_b, optimize=True)
temp -= lib.einsum('ibac,a->ibc', eris_OVVV, r_b, optimize=True)
del eris_OVVV
s[s_bbb:f_bbb] += temp[:,ab_ind_b[0],ab_ind_b[1]].reshape(-1)
del temp
temp = np.zeros((nocc_a, nvir_b, nvir_a))
if isinstance(eris.ovVV, type(None)):
chnk_size = uadc_ao2mo.calculate_chunk_size(adc)
a = 0
for p in range(0,nocc_a,chnk_size):
eris_ovVV = dfadc.get_ovvv_spin_df(
adc, eris.Lov, eris.LVV, p, chnk_size).reshape(-1,nvir_a,nvir_b,nvir_b)
k = eris_ovVV.shape[0]
s[s_b:f_b] += lib.einsum('icab,ibc->a', eris_ovVV, r_aba[a:a+k], optimize=True)
temp[a:a+k] += lib.einsum('icab,a->ibc', eris_ovVV, r_b, optimize=True)
del eris_ovVV
a += k
else :
eris_ovVV = radc_ao2mo.unpack_eri_1(eris.ovVV, nvir_b)
s[s_b:f_b] += lib.einsum('icab,ibc->a', eris_ovVV, r_aba, optimize=True)
temp += lib.einsum('icab,a->ibc', eris_ovVV, r_b, optimize=True)
del eris_ovVV
s[s_aba:f_aba] += temp.reshape(-1)
del temp
############### ADC(2) iab - jcd block ############################
s[s_aaa:f_aaa] += D_iab_a * r_aaa
s[s_bab:f_bab] += D_iab_bab * r_bab.reshape(-1)
s[s_aba:f_aba] += D_iab_aba * r_aba.reshape(-1)
s[s_bbb:f_bbb] += D_iab_b * r_bbb
############### ADC(3) iab - jcd block ############################
if (method == "adc(2)-x" or method == "adc(3)"):
eris_oovv = eris.oovv
eris_OOVV = eris.OOVV
eris_ooVV = eris.ooVV
eris_OOvv = eris.OOvv
eris_ovvo = eris.ovvo
eris_OVVO = eris.OVVO
eris_ovVO = eris.ovVO
eris_OVvo = eris.OVvo
r_aaa = r_aaa.reshape(nocc_a,-1)
r_bbb = r_bbb.reshape(nocc_b,-1)
r_aaa_u = np.zeros((nocc_a,nvir_a,nvir_a))
r_aaa_u[:,ab_ind_a[0],ab_ind_a[1]]= r_aaa.copy()
r_aaa_u[:,ab_ind_a[1],ab_ind_a[0]]= -r_aaa.copy()
r_bbb_u = None
r_bbb_u = np.zeros((nocc_b,nvir_b,nvir_b))
r_bbb_u[:,ab_ind_b[0],ab_ind_b[1]]= r_bbb.copy()
r_bbb_u[:,ab_ind_b[1],ab_ind_b[0]]= -r_bbb.copy()
if isinstance(eris.vvvv_p, np.ndarray):
eris_vvvv = eris.vvvv_p
temp_1 = np.dot(r_aaa,eris_vvvv.T)
del eris_vvvv
elif isinstance(eris.vvvv_p, list):
temp_1 = contract_r_vvvv_antisym(adc,r_aaa_u,eris.vvvv_p)
temp_1 = temp_1[:,ab_ind_a[0],ab_ind_a[1]]
else:
temp_1 = contract_r_vvvv_antisym(adc,r_aaa_u,eris.Lvv)
temp_1 = temp_1[:,ab_ind_a[0],ab_ind_a[1]]
s[s_aaa:f_aaa] += temp_1.reshape(-1)
if isinstance(eris.VVVV_p, np.ndarray):
eris_VVVV = eris.VVVV_p
temp_1 = np.dot(r_bbb,eris_VVVV.T)
del eris_VVVV
elif isinstance(eris.VVVV_p, list):
temp_1 = contract_r_vvvv_antisym(adc,r_bbb_u,eris.VVVV_p)
temp_1 = temp_1[:,ab_ind_b[0],ab_ind_b[1]]
else:
temp_1 = contract_r_vvvv_antisym(adc,r_bbb_u,eris.LVV)
temp_1 = temp_1[:,ab_ind_b[0],ab_ind_b[1]]
s[s_bbb:f_bbb] += temp_1.reshape(-1)
if isinstance(eris.vVvV_p, np.ndarray):
r_bab_t = r_bab.reshape(nocc_b,-1)
r_aba_t = r_aba.transpose(0,2,1).reshape(nocc_a,-1)
eris_vVvV = eris.vVvV_p
s[s_bab:f_bab] += np.dot(r_bab_t,eris_vVvV.T).reshape(-1)
temp_1 = np.dot(r_aba_t,eris_vVvV.T).reshape(nocc_a, nvir_a,nvir_b)
s[s_aba:f_aba] += temp_1.transpose(0,2,1).copy().reshape(-1)
elif isinstance(eris.vVvV_p, list):
temp_1 = contract_r_vvvv(adc,r_bab,eris.vVvV_p)
temp_2 = contract_r_vvvv(adc,r_aba,eris.VvVv_p)
s[s_bab:f_bab] += temp_1.reshape(-1)
s[s_aba:f_aba] += temp_2.reshape(-1)
else:
temp_1 = contract_r_vvvv(adc,r_bab,(eris.Lvv,eris.LVV))
temp_2 = contract_r_vvvv(adc,r_aba,(eris.LVV,eris.Lvv))
s[s_bab:f_bab] += temp_1.reshape(-1)
s[s_aba:f_aba] += temp_2.reshape(-1)
temp = 0.5*lib.einsum('jiyz,jzx->ixy',eris_oovv,r_aaa_u,optimize=True)
temp -= 0.5*lib.einsum('jzyi,jzx->ixy',eris_ovvo,r_aaa_u,optimize=True)
temp +=0.5*lib.einsum('jzyi,jxz->ixy',eris_OVvo,r_bab,optimize=True)
s[s_aaa:f_aaa] += temp[:,ab_ind_a[0],ab_ind_a[1]].reshape(-1)
s[s_bab:f_bab] -= 0.5*lib.einsum('jzyi,jzx->ixy',eris_ovVO,
r_aaa_u,optimize=True).reshape(-1)
s[s_bab:f_bab] -= 0.5*lib.einsum('jiyz,jxz->ixy',eris_OOVV,
r_bab,optimize=True).reshape(-1)
s[s_bab:f_bab] += 0.5*lib.einsum('jzyi,jxz->ixy',eris_OVVO,
r_bab,optimize=True).reshape(-1)
temp = 0.5*lib.einsum('jiyz,jzx->ixy',eris_OOVV,r_bbb_u,optimize=True)
temp -= 0.5*lib.einsum('jzyi,jzx->ixy',eris_OVVO,r_bbb_u,optimize=True)
temp +=0.5* lib.einsum('jzyi,jxz->ixy',eris_ovVO,r_aba,optimize=True)
s[s_bbb:f_bbb] += temp[:,ab_ind_b[0],ab_ind_b[1]].reshape(-1)
s[s_aba:f_aba] -= 0.5*lib.einsum('jiyz,jxz->ixy',eris_oovv,
r_aba,optimize=True).reshape(-1)
s[s_aba:f_aba] += 0.5*lib.einsum('jzyi,jxz->ixy',eris_ovvo,
r_aba,optimize=True).reshape(-1)
s[s_aba:f_aba] -= 0.5*lib.einsum('jzyi,jzx->ixy',eris_OVvo,
r_bbb_u,optimize=True).reshape(-1)
temp = -0.5*lib.einsum('jixz,jzy->ixy',eris_oovv,r_aaa_u,optimize=True)
temp += 0.5*lib.einsum('jzxi,jzy->ixy',eris_ovvo,r_aaa_u,optimize=True)
temp -= 0.5*lib.einsum('jzxi,jyz->ixy',eris_OVvo,r_bab,optimize=True)
s[s_aaa:f_aaa] += temp[:,ab_ind_a[0],ab_ind_a[1]].reshape(-1)
s[s_bab:f_bab] -= 0.5*lib.einsum('jixz,jzy->ixy',
eris_OOvv,r_bab,optimize=True).reshape(-1)
temp = -0.5*lib.einsum('jixz,jzy->ixy',eris_OOVV,r_bbb_u,optimize=True)
temp += 0.5*lib.einsum('jzxi,jzy->ixy',eris_OVVO,r_bbb_u,optimize=True)
temp -= 0.5*lib.einsum('jzxi,jyz->ixy',eris_ovVO,r_aba,optimize=True)
s[s_bbb:f_bbb] += temp[:,ab_ind_b[0],ab_ind_b[1]].reshape(-1)
s[s_aba:f_aba] -= 0.5*lib.einsum('jixz,jzy->ixy',eris_ooVV,
r_aba,optimize=True).reshape(-1)
temp = 0.5*lib.einsum('jixw,jyw->ixy',eris_oovv,r_aaa_u,optimize=True)
temp -= 0.5*lib.einsum('jwxi,jyw->ixy',eris_ovvo,r_aaa_u,optimize=True)
temp -= 0.5*lib.einsum('jwxi,jyw->ixy',eris_OVvo,r_bab,optimize=True)
s[s_aaa:f_aaa] += temp[:,ab_ind_a[0],ab_ind_a[1]].reshape(-1)
s[s_bab:f_bab] -= 0.5*lib.einsum('jixw,jwy->ixy',eris_OOvv,
r_bab,optimize=True).reshape(-1)
temp = 0.5*lib.einsum('jixw,jyw->ixy',eris_OOVV,r_bbb_u,optimize=True)
temp -= 0.5*lib.einsum('jwxi,jyw->ixy',eris_OVVO,r_bbb_u,optimize=True)
temp -= 0.5*lib.einsum('jwxi,jyw->ixy',eris_ovVO,r_aba,optimize=True)
s[s_bbb:f_bbb] += temp[:,ab_ind_b[0],ab_ind_b[1]].reshape(-1)
s[s_aba:f_aba] -= 0.5*lib.einsum('jixw,jwy->ixy',eris_ooVV,
r_aba,optimize=True).reshape(-1)
temp = -0.5*lib.einsum('jiyw,jxw->ixy',eris_oovv,r_aaa_u,optimize=True)
temp += 0.5*lib.einsum('jwyi,jxw->ixy',eris_ovvo,r_aaa_u,optimize=True)
temp += 0.5*lib.einsum('jwyi,jxw->ixy',eris_OVvo,r_bab,optimize=True)
s[s_aaa:f_aaa] += temp[:,ab_ind_a[0],ab_ind_a[1]].reshape(-1)
s[s_bab:f_bab] -= 0.5*lib.einsum('jiyw,jxw->ixy',eris_OOVV,
r_bab,optimize=True).reshape(-1)
s[s_bab:f_bab] += 0.5*lib.einsum('jwyi,jxw->ixy',eris_OVVO,
r_bab,optimize=True).reshape(-1)
s[s_bab:f_bab] += 0.5*lib.einsum('jwyi,jxw->ixy',eris_ovVO,
r_aaa_u,optimize=True).reshape(-1)
s[s_aba:f_aba] -= 0.5*lib.einsum('jiyw,jxw->ixy',eris_oovv,
r_aba,optimize=True).reshape(-1)
s[s_aba:f_aba] += 0.5*lib.einsum('jwyi,jxw->ixy',eris_ovvo,
r_aba,optimize=True).reshape(-1)
s[s_aba:f_aba] += 0.5*lib.einsum('jwyi,jxw->ixy',eris_OVvo,
r_bbb_u,optimize=True).reshape(-1)
temp = -0.5*lib.einsum('jiyw,jxw->ixy',eris_OOVV,r_bbb_u,optimize=True)
temp += 0.5*lib.einsum('jwyi,jxw->ixy',eris_OVVO,r_bbb_u,optimize=True)
temp += 0.5*lib.einsum('jwyi,jxw->ixy',eris_ovVO,r_aba,optimize=True)
s[s_bbb:f_bbb] += temp[:,ab_ind_b[0],ab_ind_b[1]].reshape(-1)
if (method == "adc(3)"):
#print("Calculating additional terms for adc(3)")
eris_ovoo = eris.ovoo
eris_OVOO = eris.OVOO
eris_ovOO = eris.ovOO
eris_OVoo = eris.OVoo
############### ADC(3) a - ibc block and ibc-a coupling blocks ########################
t2_1_a = adc.t2[0][0][:]
t2_1_ab = adc.t2[0][1][:]
t2_1_a_t = t2_1_a[:,:,ab_ind_a[0],ab_ind_a[1]]
r_aaa = r_aaa.reshape(nocc_a,-1)
temp = 0.5*lib.einsum('lmp,jp->lmj',t2_1_a_t,r_aaa)
del t2_1_a_t
s[s_a:f_a] += lib.einsum('lmj,lamj->a',temp, eris_ovoo, optimize=True)
s[s_a:f_a] -= lib.einsum('lmj,malj->a',temp, eris_ovoo, optimize=True)
del temp
temp_1 = -lib.einsum('lmzw,jzw->jlm',t2_1_ab,r_bab)
s[s_a:f_a] -= lib.einsum('jlm,lamj->a',temp_1, eris_ovOO, optimize=True)
del temp_1
temp_1 = -lib.einsum('mlwz,jzw->jlm',t2_1_ab,r_aba)
s[s_b:f_b] -= lib.einsum('jlm,lamj->a',temp_1, eris_OVoo, optimize=True)
del temp_1
r_aaa_u = np.zeros((nocc_a,nvir_a,nvir_a))
r_aaa_u[:,ab_ind_a[0],ab_ind_a[1]]= r_aaa.copy()
r_aaa_u[:,ab_ind_a[1],ab_ind_a[0]]= -r_aaa.copy()
r_bbb_u = np.zeros((nocc_b,nvir_b,nvir_b))
r_bbb_u[:,ab_ind_b[0],ab_ind_b[1]]= r_bbb.copy()
r_bbb_u[:,ab_ind_b[1],ab_ind_b[0]]= -r_bbb.copy()
r_bab = r_bab.reshape(nocc_b,nvir_a,nvir_b)
r_aba = r_aba.reshape(nocc_a,nvir_b,nvir_a)
temp_s_a = np.zeros_like(r_bab)
temp_s_a = lib.einsum('jlwd,jzw->lzd',t2_1_a,r_aaa_u,optimize=True)
temp_s_a += lib.einsum('ljdw,jzw->lzd',t2_1_ab,r_bab,optimize=True)
temp_s_a_1 = np.zeros_like(r_bab)
temp_s_a_1 = -lib.einsum('jlzd,jwz->lwd',t2_1_a,r_aaa_u,optimize=True)
temp_s_a_1 += -lib.einsum('ljdz,jwz->lwd',t2_1_ab,r_bab,optimize=True)
temp_1_1 = np.zeros((nocc_a,nvir_a,nvir_a))
temp_1_2 = np.zeros((nocc_a,nvir_a,nvir_a))
if isinstance(eris.ovvv, type(None)):
chnk_size = uadc_ao2mo.calculate_chunk_size(adc)
a = 0
for p in range(0,nocc_a,chnk_size):
eris_ovvv = dfadc.get_ovvv_spin_df(
adc, eris.Lov, eris.Lvv, p, chnk_size).reshape(-1,nvir_a,nvir_a,nvir_a)
k = eris_ovvv.shape[0]
s[s_a:f_a] += 0.5*lib.einsum('lzd,ldza->a',
temp_s_a[a:a+k],eris_ovvv,optimize=True)
s[s_a:f_a] -= 0.5*lib.einsum('lzd,lazd->a',
temp_s_a[a:a+k],eris_ovvv,optimize=True)
s[s_a:f_a] -= 0.5*lib.einsum('lwd,ldwa->a',
temp_s_a_1[a:a+k],eris_ovvv,optimize=True)
s[s_a:f_a] += 0.5*lib.einsum('lwd,lawd->a',
temp_s_a_1[a:a+k],eris_ovvv,optimize=True)
temp_1_1[a:a+k] += lib.einsum('ldxb,b->lxd', eris_ovvv,r_a,optimize=True)
temp_1_1[a:a+k] -= lib.einsum('lbxd,b->lxd', eris_ovvv,r_a,optimize=True)
temp_1_2[a:a+k] += lib.einsum('ldyb,b->lyd', eris_ovvv,r_a,optimize=True)
temp_1_2[a:a+k] -= lib.einsum('lbyd,b->lyd', eris_ovvv,r_a,optimize=True)
del eris_ovvv
a += k
else :
eris_ovvv = radc_ao2mo.unpack_eri_1(eris.ovvv, nvir_a)
s[s_a:f_a] += 0.5*lib.einsum('lzd,ldza->a',temp_s_a,eris_ovvv,optimize=True)
s[s_a:f_a] -= 0.5*lib.einsum('lzd,lazd->a',temp_s_a,eris_ovvv,optimize=True)
s[s_a:f_a] -= 0.5*lib.einsum('lwd,ldwa->a',temp_s_a_1,eris_ovvv,optimize=True)
s[s_a:f_a] += 0.5*lib.einsum('lwd,lawd->a',temp_s_a_1,eris_ovvv,optimize=True)
temp_1_1 += lib.einsum('ldxb,b->lxd', eris_ovvv,r_a,optimize=True)
temp_1_1 -= lib.einsum('lbxd,b->lxd', eris_ovvv,r_a,optimize=True)
temp_1_2 += lib.einsum('ldyb,b->lyd', eris_ovvv,r_a,optimize=True)
temp_1_2 -= lib.einsum('lbyd,b->lyd', eris_ovvv,r_a,optimize=True)
del eris_ovvv
del temp_s_a
del temp_s_a_1
r_bab_t = r_bab.reshape(nocc_b*nvir_a,-1)
temp = np.ascontiguousarray(t2_1_ab.transpose(
0,3,1,2)).reshape(nocc_a*nvir_b,nocc_b*nvir_a)
temp_2 = np.dot(temp,r_bab_t).reshape(nocc_a,nvir_b,nvir_b)
del temp
temp_2 = np.ascontiguousarray(temp_2.transpose(0,2,1))
temp_2_new = -lib.einsum('ljzd,jzw->lwd',t2_1_ab,r_bab,optimize=True)
temp_new_1 = np.zeros_like(r_aba)
temp_new_1 = lib.einsum('ljdw,jzw->ldz',t2_1_ab,r_bbb_u,optimize=True)
temp_new_1 += lib.einsum('jlwd,jzw->ldz',t2_1_a,r_aba,optimize=True)
temp_new_2 = np.zeros_like(r_bab)
temp_new_2 = -lib.einsum('ljdz,jwz->lwd',t2_1_ab,r_bbb_u,optimize=True)
temp_new_2 += -lib.einsum('jlzd,jwz->lwd',t2_1_a,r_aba,optimize=True)
temp_2_3 = np.zeros((nocc_a,nvir_b,nvir_a))
temp_2_4 = np.zeros((nocc_a,nvir_b,nvir_a))
temp = np.zeros((nocc_a,nvir_b,nvir_b))
if isinstance(eris.ovVV, type(None)):
chnk_size = uadc_ao2mo.calculate_chunk_size(adc)
a = 0
for p in range(0,nocc_a,chnk_size):
eris_ovVV = dfadc.get_ovvv_spin_df(
adc, eris.Lov, eris.LVV, p, chnk_size).reshape(-1,nvir_a,nvir_b,nvir_b)
k = eris_ovVV.shape[0]
s[s_a:f_a] -= 0.5*lib.einsum('lzd,lazd->a',
temp_2[a:a+k],eris_ovVV,optimize=True)
s[s_a:f_a] += 0.5*lib.einsum('lwd,lawd->a',
temp_2_new[a:a+k],eris_ovVV,optimize=True)
s[s_b:f_b] += 0.5*np.einsum('ldz,ldza->a',temp_new_1[a:a+k],eris_ovVV)
s[s_b:f_b] -= 0.5*np.einsum('lwd,ldwa->a',temp_new_2[a:a+k],eris_ovVV)
eris_ovVV = eris_ovVV.reshape(-1, nvir_a, nvir_b, nvir_b)
temp_2_3[a:a+k] += lib.einsum('ldxb,b->lxd', eris_ovVV,r_b,optimize=True)
temp_2_4[a:a+k] += lib.einsum('ldyb,b->lyd', eris_ovVV,r_b,optimize=True)
temp[a:a+k] -= lib.einsum('lbyd,b->lyd',eris_ovVV,r_a,optimize=True)
del eris_ovVV
a += k
else :
eris_ovVV = radc_ao2mo.unpack_eri_1(eris.ovVV, nvir_b)
s[s_a:f_a] -= 0.5*lib.einsum('lzd,lazd->a',temp_2,eris_ovVV,optimize=True)
s[s_a:f_a] += 0.5*lib.einsum('lwd,lawd->a',temp_2_new,eris_ovVV,optimize=True)
s[s_b:f_b] += 0.5*np.einsum('ldz,ldza->a',temp_new_1,eris_ovVV)
s[s_b:f_b] -= 0.5*np.einsum('lwd,ldwa->a',temp_new_2,eris_ovVV)
eris_ovVV = eris_ovVV.reshape(-1, nvir_a, nvir_b, nvir_b)
temp_2_3 += lib.einsum('ldxb,b->lxd', eris_ovVV,r_b,optimize=True)
temp_2_4 += lib.einsum('ldyb,b->lyd', eris_ovVV,r_b,optimize=True)
temp -= lib.einsum('lbyd,b->lyd',eris_ovVV,r_a,optimize=True)
del eris_ovVV
temp = -lib.einsum('lyd,lixd->ixy',temp,t2_1_ab,optimize=True)
s[s_bab:f_bab] -= temp.reshape(-1)
del temp
del temp_2
del temp_2_new
del temp_new_1
del temp_new_2
t2_1_a_t = t2_1_a[:,:,ab_ind_a[0],ab_ind_a[1]]
temp = lib.einsum('b,lbmi->lmi',r_a,eris_ovoo)
temp -= lib.einsum('b,mbli->lmi',r_a,eris_ovoo)
s[s_aaa:f_aaa] += 0.5*lib.einsum('lmi,lmp->ip',temp,
t2_1_a_t, optimize=True).reshape(-1)
temp = lib.einsum('lxd,ilyd->ixy',temp_1_1,t2_1_a,optimize=True)
s[s_aaa:f_aaa] += temp[:,ab_ind_a[0],ab_ind_a[1] ].reshape(-1)
temp = lib.einsum('lyd,ilxd->ixy',temp_1_2,t2_1_a,optimize=True)
s[s_aaa:f_aaa] -= temp[:,ab_ind_a[0],ab_ind_a[1] ].reshape(-1)
temp = lib.einsum('lxd,ilyd->ixy',temp_2_3,t2_1_a,optimize=True)
s[s_aba:f_aba] += temp.reshape(-1)
del t2_1_a
t2_1_b = adc.t2[0][2][:]
t2_1_b_t = t2_1_b[:,:,ab_ind_b[0],ab_ind_b[1]]
r_bbb = r_bbb.reshape(nocc_b,-1)
temp = 0.5*lib.einsum('lmp,jp->lmj',t2_1_b_t,r_bbb)
del t2_1_b_t
s[s_b:f_b] += lib.einsum('lmj,lamj->a',temp, eris_OVOO, optimize=True)
s[s_b:f_b] -= lib.einsum('lmj,malj->a',temp, eris_OVOO, optimize=True)
del temp
temp_s_b = np.zeros_like(r_aba)
temp_s_b = lib.einsum('jlwd,jzw->lzd',t2_1_b,r_bbb_u,optimize=True)
temp_s_b += lib.einsum('jlwd,jzw->lzd',t2_1_ab,r_aba,optimize=True)
temp_s_b_1 = np.zeros_like(r_aba)
temp_s_b_1 = -lib.einsum('jlzd,jwz->lwd',t2_1_b,r_bbb_u,optimize=True)
temp_s_b_1 += -lib.einsum('jlzd,jwz->lwd',t2_1_ab,r_aba,optimize=True)
temp_1_3 = np.zeros((nocc_b,nvir_b,nvir_b))
temp_1_4 = np.zeros((nocc_b,nvir_b,nvir_b))
if isinstance(eris.OVVV, type(None)):
chnk_size = uadc_ao2mo.calculate_chunk_size(adc)
a = 0
for p in range(0,nocc_b,chnk_size):
eris_OVVV = dfadc.get_ovvv_spin_df(
adc, eris.LOV, eris.LVV, p, chnk_size).reshape(-1,nvir_b,nvir_b,nvir_b)
k = eris_OVVV.shape[0]
s[s_b:f_b] += 0.5*lib.einsum('lzd,ldza->a',
temp_s_b[a:a+k],eris_OVVV,optimize=True)
s[s_b:f_b] -= 0.5*lib.einsum('lzd,lazd->a',
temp_s_b[a:a+k],eris_OVVV,optimize=True)
s[s_b:f_b] -= 0.5*lib.einsum('lwd,ldwa->a',
temp_s_b_1[a:a+k],eris_OVVV,optimize=True)
s[s_b:f_b] += 0.5*lib.einsum('lwd,lawd->a',
temp_s_b_1[a:a+k],eris_OVVV,optimize=True)
temp_1_3[a:a+k] += lib.einsum('ldxb,b->lxd', eris_OVVV,r_b,optimize=True)
temp_1_3[a:a+k] -= lib.einsum('lbxd,b->lxd', eris_OVVV,r_b,optimize=True)
temp_1_4[a:a+k] += lib.einsum('ldyb,b->lyd', eris_OVVV,r_b,optimize=True)
temp_1_4[a:a+k] -= lib.einsum('lbyd,b->lyd', eris_OVVV,r_b,optimize=True)
del eris_OVVV
a += k
else :
eris_OVVV = radc_ao2mo.unpack_eri_1(eris.OVVV, nvir_b)
s[s_b:f_b] += 0.5*lib.einsum('lzd,ldza->a',temp_s_b,eris_OVVV,optimize=True)
s[s_b:f_b] -= 0.5*lib.einsum('lzd,lazd->a',temp_s_b,eris_OVVV,optimize=True)
s[s_b:f_b] -= 0.5*lib.einsum('lwd,ldwa->a',temp_s_b_1,eris_OVVV,optimize=True)
s[s_b:f_b] += 0.5*lib.einsum('lwd,lawd->a',temp_s_b_1,eris_OVVV,optimize=True)
temp_1_3 += lib.einsum('ldxb,b->lxd', eris_OVVV,r_b,optimize=True)
temp_1_3 -= lib.einsum('lbxd,b->lxd', eris_OVVV,r_b,optimize=True)
temp_1_4 += lib.einsum('ldyb,b->lyd', eris_OVVV,r_b,optimize=True)
temp_1_4 -= lib.einsum('lbyd,b->lyd', eris_OVVV,r_b,optimize=True)
del eris_OVVV
del temp_s_b
del temp_s_b_1
temp_1 = np.zeros_like(r_bab)
temp_1= lib.einsum('jlwd,jzw->lzd',t2_1_ab,r_aaa_u,optimize=True)
temp_1 += lib.einsum('jlwd,jzw->lzd',t2_1_b,r_bab,optimize=True)
temp_2 = lib.einsum('jldw,jwz->lzd',t2_1_ab,r_aba,optimize=True)
temp_1_new = np.zeros_like(r_bab)
temp_1_new = -lib.einsum('jlzd,jwz->lwd',t2_1_ab,r_aaa_u,optimize=True)
temp_1_new += -lib.einsum('jlzd,jwz->lwd',t2_1_b,r_bab,optimize=True)
temp_2_new = -lib.einsum('jldz,jzw->lwd',t2_1_ab,r_aba,optimize=True)
temp_2_1 = np.zeros((nocc_b,nvir_a,nvir_b))
temp_2_2 = np.zeros((nocc_b,nvir_a,nvir_b))
temp = np.zeros((nocc_b,nvir_a,nvir_a))
if isinstance(eris.OVvv, type(None)):
chnk_size = uadc_ao2mo.calculate_chunk_size(adc)
a = 0
for p in range(0,nocc_b,chnk_size):
eris_OVvv = dfadc.get_ovvv_spin_df(
adc, eris.LOV, eris.Lvv, p, chnk_size).reshape(-1,nvir_b,nvir_a,nvir_a)
k = eris_OVvv.shape[0]
s[s_a:f_a] += 0.5*lib.einsum('lzd,ldza->a',
temp_1[a:a+k],eris_OVvv,optimize=True)
s[s_b:f_b] -= 0.5*lib.einsum('lzd,lazd->a',
temp_2[a:a+k],eris_OVvv,optimize=True)
s[s_a:f_a] -= 0.5*lib.einsum('lwd,ldwa->a',
temp_1_new[a:a+k],eris_OVvv,optimize=True)
s[s_b:f_b] += 0.5*lib.einsum('lwd,lawd->a',
temp_2_new[a:a+k],eris_OVvv,optimize=True)
temp_2_1[a:a+k] += lib.einsum('ldxb,b->lxd', eris_OVvv,r_a,optimize=True)
temp_2_2[a:a+k] += lib.einsum('ldyb,b->lyd', eris_OVvv,r_a,optimize=True)
temp[a:a+k] -= lib.einsum('lbyd,b->lyd',eris_OVvv,r_b,optimize=True)
del eris_OVvv
a += k
else :
eris_OVvv = radc_ao2mo.unpack_eri_1(eris.OVvv, nvir_a)
s[s_a:f_a] += 0.5*lib.einsum('lzd,ldza->a',temp_1,eris_OVvv,optimize=True)
s[s_b:f_b] -= 0.5*lib.einsum('lzd,lazd->a',temp_2,eris_OVvv,optimize=True)
s[s_a:f_a] -= 0.5*lib.einsum('lwd,ldwa->a',temp_1_new,eris_OVvv,optimize=True)
s[s_b:f_b] += 0.5*lib.einsum('lwd,lawd->a',temp_2_new,eris_OVvv,optimize=True)
temp_2_1 += lib.einsum('ldxb,b->lxd', eris_OVvv,r_a,optimize=True)
temp_2_2 += lib.einsum('ldyb,b->lyd', eris_OVvv,r_a,optimize=True)
temp -= lib.einsum('lbyd,b->lyd',eris_OVvv,r_b,optimize=True)
del eris_OVvv
temp_new = -lib.einsum('lyd,ildx->ixy',temp,t2_1_ab,optimize=True)
s[s_aba:f_aba] -= temp_new.reshape(-1)
del temp
del temp_new
del temp_1
del temp_1_new
del temp_2
del temp_2_new
t2_1_b_t = t2_1_b[:,:,ab_ind_b[0],ab_ind_b[1]]
temp = lib.einsum('b,lbmi->lmi',r_b,eris_OVOO)
temp -= lib.einsum('b,mbli->lmi',r_b,eris_OVOO)
s[s_bbb:f_bbb] += 0.5*lib.einsum('lmi,lmp->ip',temp,
t2_1_b_t, optimize=True).reshape(-1)
temp = lib.einsum('lxd,ilyd->ixy',temp_2_1,t2_1_b,optimize=True)
s[s_bab:f_bab] += temp.reshape(-1)
temp = lib.einsum('lxd,ilyd->ixy',temp_1_3,t2_1_b,optimize=True)
s[s_bbb:f_bbb] += temp[:,ab_ind_b[0],ab_ind_b[1] ].reshape(-1)
temp = lib.einsum('lyd,ilxd->ixy',temp_1_4,t2_1_b,optimize=True)
s[s_bbb:f_bbb] -= temp[:,ab_ind_b[0],ab_ind_b[1] ].reshape(-1)
del t2_1_b
temp_1 = lib.einsum('b,lbmi->lmi',r_a,eris_ovOO)
s[s_bab:f_bab] += lib.einsum('lmi,lmxy->ixy',temp_1, t2_1_ab, optimize=True).reshape(-1)
temp_1 = lib.einsum('b,lbmi->mli',r_b,eris_OVoo)
s[s_aba:f_aba] += lib.einsum('mli,mlyx->ixy',temp_1, t2_1_ab, optimize=True).reshape(-1)
temp = lib.einsum('lxd,ilyd->ixy',temp_2_1,t2_1_ab,optimize=True)
s[s_aaa:f_aaa] += temp[:,ab_ind_a[0],ab_ind_a[1] ].reshape(-1)
temp = lib.einsum('lyd,ilxd->ixy',temp_2_2,t2_1_ab,optimize=True)
s[s_aaa:f_aaa] -= temp[:,ab_ind_a[0],ab_ind_a[1] ].reshape(-1)
temp = lib.einsum('lxd,lidy->ixy',temp_1_1,t2_1_ab,optimize=True)
s[s_bab:f_bab] += temp.reshape(-1)
temp = lib.einsum('lxd,lidy->ixy',temp_2_3,t2_1_ab,optimize=True)
s[s_bbb:f_bbb] += temp[:,ab_ind_b[0],ab_ind_b[1] ].reshape(-1)
temp = lib.einsum('lyd,lidx->ixy',temp_2_4,t2_1_ab,optimize=True)
s[s_bbb:f_bbb] -= temp[:,ab_ind_b[0],ab_ind_b[1] ].reshape(-1)
temp = lib.einsum('lxd,ilyd->ixy',temp_1_3,t2_1_ab,optimize=True)
s[s_aba:f_aba] += temp.reshape(-1)
del t2_1_ab
cput0 = log.timer_debug1("completed sigma vector calculation", *cput0)
return s
del temp_2_1
del temp_1_3
del temp_1_4
del temp_1_1
del temp_1_2
del temp_2_3
return sigma_
[docs]
def get_trans_moments(adc):
cput0 = (logger.process_clock(), logger.perf_counter())
log = logger.Logger(adc.stdout, adc.verbose)
nmo_a = adc.nmo_a
nmo_b = adc.nmo_b
T_a = []
T_b = []
for orb in range(nmo_a):
T_aa = get_trans_moments_orbital(adc,orb, spin="alpha")
T_a.append(T_aa)
for orb in range(nmo_b):
T_bb = get_trans_moments_orbital(adc,orb, spin="beta")
T_b.append(T_bb)
cput0 = log.timer_debug1("completed spec vector calc in ADC(3) calculation", *cput0)
return (T_a, T_b)
[docs]
def get_trans_moments_orbital(adc, orb, spin="alpha"):
if adc.method not in ("adc(2)", "adc(2)-x", "adc(3)"):
raise NotImplementedError(adc.method)
method = adc.method
if (adc.approx_trans_moments is False or adc.method == "adc(3)"):
t1_2_a, t1_2_b = adc.t1[0]
nocc_a = adc.nocc_a
nocc_b = adc.nocc_b
nvir_a = adc.nvir_a
nvir_b = adc.nvir_b
ab_ind_a = np.tril_indices(nvir_a, k=-1)
ab_ind_b = np.tril_indices(nvir_b, k=-1)
n_singles_a = nvir_a
n_singles_b = nvir_b
n_doubles_aaa = nvir_a* (nvir_a - 1) * nocc_a // 2
n_doubles_bab = nocc_b * nvir_a* nvir_b
n_doubles_aba = nocc_a * nvir_b* nvir_a
n_doubles_bbb = nvir_b* (nvir_b - 1) * nocc_b // 2
dim = n_singles_a + n_singles_b + n_doubles_aaa + n_doubles_bab + n_doubles_aba + n_doubles_bbb
idn_vir_a = np.identity(nvir_a)
idn_vir_b = np.identity(nvir_b)
s_a = 0
f_a = n_singles_a
s_b = f_a
f_b = s_b + n_singles_b
s_aaa = f_b
f_aaa = s_aaa + n_doubles_aaa
s_bab = f_aaa
f_bab = s_bab + n_doubles_bab
s_aba = f_bab
f_aba = s_aba + n_doubles_aba
s_bbb = f_aba
f_bbb = s_bbb + n_doubles_bbb
T = np.zeros((dim))
######## spin = alpha ############################################
if spin == "alpha":
pass # placehold
######## ADC(2) part ############################################
t2_1_a = adc.t2[0][0][:]
t2_1_ab = adc.t2[0][1][:]
if orb < nocc_a:
if (adc.approx_trans_moments is False or adc.method == "adc(3)"):
T[s_a:f_a] = -t1_2_a[orb,:]
t2_1_t = t2_1_a[:,:,ab_ind_a[0],ab_ind_a[1]].copy()
t2_1_ab_t = -t2_1_ab.transpose(1,0,2,3)
T[s_aaa:f_aaa] += t2_1_t[:,orb,:].reshape(-1)
T[s_bab:f_bab] += t2_1_ab_t[:,orb,:,:].reshape(-1)
else:
T[s_a:f_a] += idn_vir_a[(orb-nocc_a), :]
T[s_a:f_a] -= 0.25*lib.einsum('klc,klac->a',t2_1_a[:,:,
(orb-nocc_a),:], t2_1_a, optimize=True)
T[s_a:f_a] -= 0.25*lib.einsum('klc,klac->a',t2_1_ab[:,:,
(orb-nocc_a),:], t2_1_ab, optimize=True)
T[s_a:f_a] -= 0.25*lib.einsum('lkc,lkac->a',t2_1_ab[:,:,
(orb-nocc_a),:], t2_1_ab, optimize=True)
######## ADC(3) 2p-1h part ############################################
if (adc.method == "adc(2)-x" and adc.approx_trans_moments is False) or (adc.method == "adc(3)"):
t2_2_a = adc.t2[1][0][:]
t2_2_ab = adc.t2[1][1][:]
if orb < nocc_a:
t2_2_t = t2_2_a[:,:,ab_ind_a[0],ab_ind_a[1]].copy()
t2_2_ab_t = -t2_2_ab.transpose(1,0,2,3)
T[s_aaa:f_aaa] += t2_2_t[:,orb,:].reshape(-1)
T[s_bab:f_bab] += t2_2_ab_t[:,orb,:,:].reshape(-1)
######### ADC(3) 1p part ############################################
if (method=='adc(3)'):
if (adc.approx_trans_moments is False):
t1_3_a, t1_3_b = adc.t1[1]
if orb < nocc_a:
T[s_a:f_a] += 0.5*lib.einsum('kac,ck->a',t2_1_a[:,orb,:,:], t1_2_a.T,optimize=True)
T[s_a:f_a] -= 0.5*lib.einsum('kac,ck->a',t2_1_ab[orb,:,:,:], t1_2_b.T,optimize=True)
if (adc.approx_trans_moments is False):
T[s_a:f_a] -= t1_3_a[orb,:]
else:
T[s_a:f_a] -= 0.25*lib.einsum('klc,klac->a',
t2_1_a[:,:,(orb-nocc_a),:], t2_2_a, optimize=True)
T[s_a:f_a] -= 0.25*lib.einsum('klc,klac->a',
t2_1_ab[:,:,(orb-nocc_a),:], t2_2_ab, optimize=True)
T[s_a:f_a] -= 0.25*lib.einsum('lkc,lkac->a',
t2_1_ab[:,:,(orb-nocc_a),:], t2_2_ab, optimize=True)
T[s_a:f_a] -= 0.25*lib.einsum('klac,klc->a',t2_1_a,
t2_2_a[:,:,(orb-nocc_a),:],optimize=True)
T[s_a:f_a] -= 0.25*lib.einsum('klac,klc->a',t2_1_ab,
t2_2_ab[:,:,(orb-nocc_a),:],optimize=True)
T[s_a:f_a] -= 0.25*lib.einsum('lkac,lkc->a',t2_1_ab,
t2_2_ab[:,:,(orb-nocc_a),:],optimize=True)
del t2_2_a
del t2_2_ab
del t2_1_a
del t2_1_ab
######### spin = beta ############################################
else:
pass # placehold
t2_1_b = adc.t2[0][2][:]
t2_1_ab = adc.t2[0][1][:]
if orb < nocc_b:
if (adc.approx_trans_moments is False or adc.method == "adc(3)"):
T[s_b:f_b] = -t1_2_b[orb,:]
t2_1_t = t2_1_b[:,:,ab_ind_b[0],ab_ind_b[1]].copy()
t2_1_ab_t = -t2_1_ab.transpose(0,1,3,2)
T[s_bbb:f_bbb] += t2_1_t[:,orb,:].reshape(-1)
T[s_aba:f_aba] += t2_1_ab_t[:,orb,:,:].reshape(-1)
else:
T[s_b:f_b] += idn_vir_b[(orb-nocc_b), :]
T[s_b:f_b] -= 0.25*lib.einsum('klc,klac->a',t2_1_b[:,:,
(orb-nocc_b),:], t2_1_b, optimize=True)
T[s_b:f_b] -= 0.25*lib.einsum('lkc,lkca->a',t2_1_ab[:,:,:,
(orb-nocc_b)], t2_1_ab, optimize=True)
T[s_b:f_b] -= 0.25*lib.einsum('lkc,lkca->a',t2_1_ab[:,:,:,
(orb-nocc_b)], t2_1_ab, optimize=True)
######### ADC(3) 2p-1h part ############################################
if (adc.method == "adc(2)-x" and adc.approx_trans_moments is False) or (adc.method == "adc(3)"):
t2_2_ab = adc.t2[1][1][:]
t2_2_b = adc.t2[1][2][:]
if orb < nocc_b:
t2_2_t = t2_2_b[:,:,ab_ind_b[0],ab_ind_b[1]].copy()
t2_2_ab_t = -t2_2_ab.transpose(0,1,3,2)
T[s_bbb:f_bbb] += t2_2_t[:,orb,:].reshape(-1)
T[s_aba:f_aba] += t2_2_ab_t[:,orb,:,:].reshape(-1)
######### ADC(2) 1p part ############################################
if(method=='adc(3)'):
if (adc.approx_trans_moments is False):
t1_3_a, t1_3_b = adc.t1[1]
if orb < nocc_b:
T[s_b:f_b] += 0.5*lib.einsum('kac,ck->a',t2_1_b[:,orb,:,:], t1_2_b.T,optimize=True)
T[s_b:f_b] -= 0.5*lib.einsum('kca,ck->a',t2_1_ab[:,orb,:,:], t1_2_a.T,optimize=True)
if (adc.approx_trans_moments is False):
T[s_b:f_b] -= t1_3_b[orb,:]
else:
T[s_b:f_b] -= 0.25*lib.einsum('klc,klac->a',
t2_1_b[:,:,(orb-nocc_b),:], t2_2_b, optimize=True)
T[s_b:f_b] -= 0.25*lib.einsum('lkc,lkca->a',
t2_1_ab[:,:,:,(orb-nocc_b)], t2_2_ab, optimize=True)
T[s_b:f_b] -= 0.25*lib.einsum('lkc,lkca->a',
t2_1_ab[:,:,:,(orb-nocc_b)], t2_2_ab, optimize=True)
T[s_b:f_b] -= 0.25*lib.einsum('klac,klc->a',t2_1_b,
t2_2_b[:,:,(orb-nocc_b),:],optimize=True)
T[s_b:f_b] -= 0.25*lib.einsum('lkca,lkc->a',t2_1_ab,
t2_2_ab[:,:,:,(orb-nocc_b)],optimize=True)
T[s_b:f_b] -= 0.25*lib.einsum('klca,klc->a',t2_1_ab,
t2_2_ab[:,:,:,(orb-nocc_b)],optimize=True)
del t2_2_b
del t2_2_ab
del t2_1_b
del t2_1_ab
return T
[docs]
def analyze_eigenvector(adc):
nocc_a = adc.nocc_a
nocc_b = adc.nocc_b
nvir_a = adc.nvir_a
nvir_b = adc.nvir_b
evec_print_tol = adc.evec_print_tol
logger.info(adc, "Number of alpha occupied orbitals = %d", nocc_a)
logger.info(adc, "Number of beta occupied orbitals = %d", nocc_b)
logger.info(adc, "Number of alpha virtual orbitals = %d", nvir_a)
logger.info(adc, "Number of beta virtual orbitals = %d", nvir_b)
logger.info(adc, "Print eigenvector elements > %f\n", evec_print_tol)
ab_a = np.tril_indices(nvir_a, k=-1)
ab_b = np.tril_indices(nvir_b, k=-1)
n_singles_a = nvir_a
n_singles_b = nvir_b
n_doubles_aaa = nvir_a* (nvir_a - 1) * nocc_a // 2
n_doubles_bab = nocc_b * nvir_a* nvir_b
n_doubles_aba = nocc_a * nvir_b* nvir_a
n_doubles_bbb = nvir_b* (nvir_b - 1) * nocc_b // 2
s_a = 0
f_a = n_singles_a
s_b = f_a
f_b = s_b + n_singles_b
s_aaa = f_b
f_aaa = s_aaa + n_doubles_aaa
s_bab = f_aaa
f_bab = s_bab + n_doubles_bab
s_aba = f_bab
f_aba = s_aba + n_doubles_aba
s_bbb = f_aba
f_bbb = s_bbb + n_doubles_bbb
U = adc.U
for I in range(U.shape[1]):
U1 = U[:f_b, I]
U2 = U[f_b:, I]
U1dotU1 = np.dot(U1, U1)
U2dotU2 = np.dot(U2, U2)
temp_aaa = np.zeros((nocc_a, nvir_a, nvir_a))
temp_aaa[:,ab_a[0],ab_a[1]] = U[s_aaa:f_aaa,I].reshape(nocc_a,-1).copy()
temp_aaa[:,ab_a[1],ab_a[0]] = -U[s_aaa:f_aaa,I].reshape(nocc_a,-1).copy()
U_aaa = temp_aaa.reshape(-1).copy()
temp_bbb = np.zeros((nocc_b, nvir_b, nvir_b))
temp_bbb[:,ab_b[0],ab_b[1]] = U[s_bbb:f_bbb,I].reshape(nocc_b,-1).copy()
temp_bbb[:,ab_b[1],ab_b[0]] = -U[s_bbb:f_bbb,I].reshape(nocc_b,-1).copy()
U_bbb = temp_bbb.reshape(-1).copy()
U_sq = U[:,I].copy()**2
ind_idx = np.argsort(-U_sq)
U_sq = U_sq[ind_idx]
U_sorted = U[ind_idx,I].copy()
U_sq_aaa = U_aaa.copy()**2
U_sq_bbb = U_bbb.copy()**2
ind_idx_aaa = np.argsort(-U_sq_aaa)
ind_idx_bbb = np.argsort(-U_sq_bbb)
U_sq_aaa = U_sq_aaa[ind_idx_aaa]
U_sq_bbb = U_sq_bbb[ind_idx_bbb]
U_sorted_aaa = U_aaa[ind_idx_aaa].copy()
U_sorted_bbb = U_bbb[ind_idx_bbb].copy()
U_sorted = U_sorted[U_sq > evec_print_tol**2]
ind_idx = ind_idx[U_sq > evec_print_tol**2]
U_sorted_aaa = U_sorted_aaa[U_sq_aaa > evec_print_tol**2]
U_sorted_bbb = U_sorted_bbb[U_sq_bbb > evec_print_tol**2]
ind_idx_aaa = ind_idx_aaa[U_sq_aaa > evec_print_tol**2]
ind_idx_bbb = ind_idx_bbb[U_sq_bbb > evec_print_tol**2]
singles_a_idx = []
singles_b_idx = []
doubles_aaa_idx = []
doubles_bab_idx = []
doubles_aba_idx = []
doubles_bbb_idx = []
singles_a_val = []
singles_b_val = []
doubles_bab_val = []
doubles_aba_val = []
iter_idx = 0
for orb_idx in ind_idx:
if orb_idx in range(s_a,f_a):
a_idx = orb_idx + 1 + nocc_a
singles_a_idx.append(a_idx)
singles_a_val.append(U_sorted[iter_idx])
if orb_idx in range(s_b,f_b):
a_idx = orb_idx - s_b + 1 + nocc_b
singles_b_idx.append(a_idx)
singles_b_val.append(U_sorted[iter_idx])
if orb_idx in range(s_bab,f_bab):
iab_idx = orb_idx - s_bab
ab_rem = iab_idx % (nvir_a*nvir_b)
i_idx = iab_idx//(nvir_a*nvir_b)
a_idx = ab_rem//nvir_b
b_idx = ab_rem % nvir_b
doubles_bab_idx.append((i_idx + 1, a_idx + 1 + nocc_a, b_idx + 1 + nocc_b))
doubles_bab_val.append(U_sorted[iter_idx])
if orb_idx in range(s_aba,f_aba):
iab_idx = orb_idx - s_aba
ab_rem = iab_idx % (nvir_b*nvir_a)
i_idx = iab_idx//(nvir_b*nvir_a)
a_idx = ab_rem//nvir_a
b_idx = ab_rem % nvir_a
doubles_aba_idx.append((i_idx + 1, a_idx + 1 + nocc_b, b_idx + 1 + nocc_a))
doubles_aba_val.append(U_sorted[iter_idx])
iter_idx += 1
for orb_aaa in ind_idx_aaa:
ab_rem = orb_aaa % (nvir_a*nvir_a)
i_idx = orb_aaa//(nvir_a*nvir_a)
a_idx = ab_rem//nvir_a
b_idx = ab_rem % nvir_a
doubles_aaa_idx.append((i_idx + 1, a_idx + 1 + nocc_a, b_idx + 1 + nocc_a))
for orb_bbb in ind_idx_bbb:
ab_rem = orb_bbb % (nvir_b*nvir_b)
i_idx = orb_bbb//(nvir_b*nvir_b)
a_idx = ab_rem//nvir_b
b_idx = ab_rem % nvir_b
doubles_bbb_idx.append((i_idx + 1, a_idx + 1 + nocc_b, b_idx + 1 + nocc_b))
doubles_aaa_val = list(U_sorted_aaa)
doubles_bbb_val = list(U_sorted_bbb)
logger.info(adc,'%s | root %d | norm(1p) = %6.4f | norm(1h2p) = %6.4f ',
adc.method ,I, U1dotU1, U2dotU2)
if singles_a_val:
logger.info(adc, "\n1p(alpha) block: ")
logger.info(adc, " a U(a)")
logger.info(adc, "------------------")
for idx, print_singles in enumerate(singles_a_idx):
logger.info(adc, ' %4d %7.4f', print_singles, singles_a_val[idx])
if singles_b_val:
logger.info(adc, "\n1p(beta) block: ")
logger.info(adc, " a U(a)")
logger.info(adc, "------------------")
for idx, print_singles in enumerate(singles_b_idx):
logger.info(adc, ' %4d %7.4f', print_singles, singles_b_val[idx])
if doubles_aaa_val:
logger.info(adc, "\n1h2p(alpha|alpha|alpha) block: ")
logger.info(adc, " i a b U(i,a,b)")
logger.info(adc, "-------------------------------")
for idx, print_doubles in enumerate(doubles_aaa_idx):
logger.info(adc, ' %4d %4d %4d %7.4f',
print_doubles[0], print_doubles[1], print_doubles[2], doubles_aaa_val[idx])
if doubles_bab_val:
logger.info(adc, "\n1h2p(beta|alpha|beta) block: ")
logger.info(adc, " i a b U(i,a,b)")
logger.info(adc, "-------------------------------")
for idx, print_doubles in enumerate(doubles_bab_idx):
logger.info(adc, ' %4d %4d %4d %7.4f',
print_doubles[0], print_doubles[1], print_doubles[2], doubles_bab_val[idx])
if doubles_aba_val:
logger.info(adc, "\n1h2p(alpha|beta|alpha) block: ")
logger.info(adc, " i a b U(i,a,b)")
logger.info(adc, "-------------------------------")
for idx, print_doubles in enumerate(doubles_aba_idx):
logger.info(adc, ' %4d %4d %4d %7.4f',
print_doubles[0], print_doubles[1], print_doubles[2], doubles_aba_val[idx])
if doubles_bbb_val:
logger.info(adc, "\n1h2p(beta|beta|beta) block: ")
logger.info(adc, " i a b U(i,a,b)")
logger.info(adc, "-------------------------------")
for idx, print_doubles in enumerate(doubles_bbb_idx):
logger.info(adc, ' %4d %4d %4d %7.4f',
print_doubles[0], print_doubles[1], print_doubles[2], doubles_bbb_val[idx])
logger.info(adc, "\n*************************************************************\n")
[docs]
def analyze_spec_factor(adc):
X_a = adc.X[0]
X_b = adc.X[1]
logger.info(adc, "Print spectroscopic factors > %E\n", adc.spec_factor_print_tol)
X_tot = (X_a, X_b)
for iter_idx, X in enumerate(X_tot):
if iter_idx == 0:
spin = "alpha"
else:
spin = "beta"
X_2 = (X.copy()**2)
thresh = adc.spec_factor_print_tol
for i in range(X_2.shape[1]):
sort = np.argsort(-X_2[:,i])
X_2_row = X_2[:,i]
X_2_row = X_2_row[sort]
if not adc.mol.symmetry:
sym = np.repeat(['A'], X_2_row.shape[0])
else:
if spin == "alpha":
sym = [symm.irrep_id2name(adc.mol.groupname, x)
for x in adc._scf.mo_coeff[0].orbsym]
sym = np.array(sym)
else:
sym = [symm.irrep_id2name(adc.mol.groupname, x)
for x in adc._scf.mo_coeff[1].orbsym]
sym = np.array(sym)
sym = sym[sort]
spec_Contribution = X_2_row[X_2_row > thresh]
index_mo = sort[X_2_row > thresh]+1
if np.sum(spec_Contribution) == 0.0:
continue
logger.info(adc, '%s | root %d %s\n', adc.method, i, spin)
logger.info(adc, " HF MO Spec. Contribution Orbital symmetry")
logger.info(adc, "-----------------------------------------------------------")
for c in range(index_mo.shape[0]):
logger.info(adc, ' %3.d %10.8f %s',
index_mo[c], spec_Contribution[c], sym[c])
logger.info(adc, '\nPartial spec. factor sum = %10.8f', np.sum(spec_Contribution))
logger.info(adc, "\n*************************************************************\n")
[docs]
def get_properties(adc, nroots=1):
#Transition moments
T = adc.get_trans_moments()
T_a = T[0]
T_b = T[1]
T_a = np.array(T_a)
T_b = np.array(T_b)
U = adc.U
#Spectroscopic amplitudes
X_a = np.dot(T_a, U).reshape(-1,nroots)
X_b = np.dot(T_b, U).reshape(-1,nroots)
X = (X_a,X_b)
#Spectroscopic factors
P = lib.einsum("pi,pi->i", X_a, X_a)
P += lib.einsum("pi,pi->i", X_b, X_b)
return P, X
[docs]
def analyze(myadc):
header = ("\n*************************************************************"
"\n Eigenvector analysis summary"
"\n*************************************************************")
logger.info(myadc, header)
myadc.analyze_eigenvector()
if myadc.compute_properties:
header = ("\n*************************************************************"
"\n Spectroscopic factors analysis summary"
"\n*************************************************************")
logger.info(myadc, header)
myadc.analyze_spec_factor()
[docs]
def compute_dyson_mo(myadc):
X_a = myadc.X[0]
X_b = myadc.X[1]
if X_a is None:
nroots = myadc.U.shape[1]
P,X_a,X_b = myadc.get_properties(nroots)
nroots = X_a.shape[1]
dyson_mo_a = np.dot(myadc.mo_coeff[0],X_a)
dyson_mo_b = np.dot(myadc.mo_coeff[1],X_b)
dyson_mo = (dyson_mo_a,dyson_mo_b)
return dyson_mo
[docs]
class UADCEA(uadc.UADC):
'''unrestricted ADC for EA energies and spectroscopic amplitudes
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`
incore_complete : bool
Avoid all I/O. Default is False.
method : string
nth-order ADC method. Options are : ADC(2), ADC(2)-X, ADC(3). Default is ADC(2).
conv_tol : float
Convergence threshold for Davidson iterations. Default is 1e-12.
max_cycle : int
Number of Davidson iterations. Default is 50.
max_space : int
Space size to hold trial vectors for Davidson iterative diagonalization. Default is 12.
Kwargs:
nroots : int
Number of roots (eigenvalues) requested. Default value is 1.
>>> myadc = adc.UADC(mf).run()
>>> myadcea = adc.UADC(myadc).run()
Saved results
e_ea : float or list of floats
EA energy (eigenvalue). For nroots = 1, it is a single float
number. If nroots > 1, it is a list of floats for the lowest
nroots eigenvalues.
v_ip : array
Eigenvectors for each EA transition.
p_ea : float
Spectroscopic amplitudes for each EA transition.
'''
_keys = {
'tol_residual','conv_tol', 'e_corr', 'method',
'method_type', 'mo_coeff', 'mo_energy_b', 'max_memory',
't1', 'mo_energy_a', 'max_space', 't2', 'max_cycle',
'nocc_a', 'nocc_b', 'nvir_a', 'nvir_b', 'mo_coeff', 'mo_energy_a',
'mo_energy_b', 'nmo_a', 'nmo_b', 'mol', 'transform_integrals',
'with_df', 'spec_factor_print_tol', 'evec_print_tol',
'compute_properties', 'approx_trans_moments', 'E', 'U', 'P', 'X',
}
def __init__(self, adc):
self.mol = adc.mol
self.verbose = adc.verbose
self.stdout = adc.stdout
self.max_memory = adc.max_memory
self.max_space = adc.max_space
self.max_cycle = adc.max_cycle
self.conv_tol = adc.conv_tol
self.tol_residual = adc.tol_residual
self.t1 = adc.t1
self.t2 = adc.t2
self.imds = adc.imds
self.e_corr = adc.e_corr
self.method = adc.method
self.method_type = adc.method_type
self._scf = adc._scf
self._nocc = adc._nocc
self._nvir = adc._nvir
self._nmo = adc._nmo
self.nocc_a = adc._nocc[0]
self.nocc_b = adc._nocc[1]
self.nvir_a = adc._nvir[0]
self.nvir_b = adc._nvir[1]
self.mo_coeff = adc.mo_coeff
self.mo_energy_a = adc.mo_energy_a
self.mo_energy_b = adc.mo_energy_b
self.nmo_a = adc._nmo[0]
self.nmo_b = adc._nmo[1]
self.transform_integrals = adc.transform_integrals
self.with_df = adc.with_df
self.compute_properties = adc.compute_properties
self.approx_trans_moments = adc.approx_trans_moments
self.spec_factor_print_tol = adc.spec_factor_print_tol
self.evec_print_tol = adc.evec_print_tol
self.E = adc.E
self.U = adc.U
self.P = adc.P
self.X = adc.X
kernel = uadc.kernel
get_imds = get_imds
matvec = matvec
get_diag = get_diag
get_trans_moments = get_trans_moments
get_properties = get_properties
analyze = analyze
analyze_spec_factor = analyze_spec_factor
analyze_eigenvector = analyze_eigenvector
compute_dyson_mo = compute_dyson_mo
[docs]
def get_init_guess(self, nroots=1, diag=None, ascending=True):
if diag is None :
diag = self.get_diag()
idx = None
if ascending:
idx = np.argsort(diag)
else:
idx = np.argsort(diag)[::-1]
guess = np.zeros((diag.shape[0], nroots))
min_shape = min(diag.shape[0], nroots)
guess[:min_shape,:min_shape] = np.identity(min_shape)
g = np.zeros((diag.shape[0], nroots))
g[idx] = guess.copy()
guess = []
for p in range(g.shape[1]):
guess.append(g[:,p])
return guess
[docs]
def gen_matvec(self, imds=None, eris=None):
if imds is None:
imds = self.get_imds(eris)
diag = self.get_diag(imds,eris)
matvec = self.matvec(imds, eris)
#matvec = lambda x: self.matvec()
return matvec, diag
[docs]
def contract_r_vvvv_antisym(myadc,r2,vvvv_d):
nocc = r2.shape[0]
nvir = r2.shape[1]
nv_pair = nvir * (nvir - 1) // 2
tril_idx = np.tril_indices(nvir, k=-1)
r2 = r2[:,tril_idx[0],tril_idx[1]]
r2 = np.ascontiguousarray(r2.reshape(nocc,-1))
r2_vvvv = np.zeros((nocc,nvir,nvir))
chnk_size = uadc_ao2mo.calculate_chunk_size(myadc)
a = 0
if isinstance(vvvv_d,list):
for dataset in vvvv_d:
k = dataset.shape[0]
dataset = dataset[:].reshape(-1,nv_pair)
r2_vvvv[:,a:a+k] = np.dot(r2,dataset.T).reshape(nocc,-1,nvir)
a += k
elif getattr(myadc, 'with_df', None):
for p in range(0,nvir,chnk_size):
vvvv = dfadc.get_vvvv_antisym_df(myadc, vvvv_d, p, chnk_size)
k = vvvv.shape[0]
vvvv = vvvv.reshape(-1,nv_pair)
r2_vvvv[:,a:a+k] = np.dot(r2,vvvv.T).reshape(nocc,-1,nvir)
del vvvv
a += k
else:
raise Exception("Unknown vvvv type")
return r2_vvvv
[docs]
def contract_r_vvvv(myadc,r2,vvvv_d):
nocc_1 = r2.shape[0]
nvir_1 = r2.shape[1]
nvir_2 = r2.shape[2]
r2 = r2.reshape(-1,nvir_1*nvir_2)
r2_vvvv = np.zeros((nocc_1,nvir_1,nvir_2))
chnk_size = uadc_ao2mo.calculate_chunk_size(myadc)
a = 0
if isinstance(vvvv_d, list):
for dataset in vvvv_d:
k = dataset.shape[0]
dataset = dataset[:].reshape(-1,nvir_1*nvir_2)
r2_vvvv[:,a:a+k] = np.dot(r2,dataset.T).reshape(nocc_1,-1,nvir_2)
a += k
elif getattr(myadc, 'with_df', None):
Lvv = vvvv_d[0]
LVV = vvvv_d[1]
for p in range(0,nvir_1,chnk_size):
vvvv = dfadc.get_vVvV_df(myadc, Lvv, LVV, p, chnk_size)
k = vvvv.shape[0]
vvvv = vvvv.reshape(-1,nvir_1*nvir_2)
r2_vvvv[:,a:a+k] = np.dot(r2,vvvv.T).reshape(nocc_1,-1,nvir_2)
del vvvv
a += k
else:
raise Exception("Unknown vvvv type")
return r2_vvvv
