#!/usr/bin/env python
# Copyright 2014-2019 The PySCF Developers. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Author: Qiming Sun <osirpt.sun@gmail.com>
#
'''
Non-relativistic UKS analytical Hessian
'''
import numpy
from pyscf import lib
from pyscf.lib import logger
from pyscf.grad import rks as rks_grad
from pyscf.hessian import rhf as rhf_hess
from pyscf.hessian import uhf as uhf_hess
from pyscf.hessian import rks as rks_hess
from pyscf.dft import numint
_get_jk = rhf_hess._get_jk
[docs]
def partial_hess_elec(hessobj, mo_energy=None, mo_coeff=None, mo_occ=None,
atmlst=None, max_memory=4000, verbose=None):
log = logger.new_logger(hessobj, verbose)
time0 = t1 = (logger.process_clock(), logger.perf_counter())
mol = hessobj.mol
mf = hessobj.base
ni = mf._numint
if mf.nlc or ni.libxc.is_nlc(mf.xc):
raise NotImplementedError('RKS Hessian for NLC functional')
if mo_energy is None: mo_energy = mf.mo_energy
if mo_occ is None: mo_occ = mf.mo_occ
if mo_coeff is None: mo_coeff = mf.mo_coeff
if atmlst is None: atmlst = range(mol.natm)
nao, nmo = mo_coeff[0].shape
mocca = mo_coeff[0][:,mo_occ[0]>0]
moccb = mo_coeff[1][:,mo_occ[1]>0]
dm0a = numpy.dot(mocca, mocca.T)
dm0b = numpy.dot(moccb, moccb.T)
# Energy weighted density matrix
mo_ea = mo_energy[0][mo_occ[0]>0]
mo_eb = mo_energy[1][mo_occ[1]>0]
dme0 = numpy.einsum('pi,qi,i->pq', mocca, mocca, mo_ea)
dme0+= numpy.einsum('pi,qi,i->pq', moccb, moccb, mo_eb)
omega, alpha, hyb = ni.rsh_and_hybrid_coeff(mf.xc, spin=mol.spin)
hybrid = ni.libxc.is_hybrid_xc(mf.xc)
de2, ej, ek = uhf_hess._partial_hess_ejk(hessobj, mo_energy, mo_coeff, mo_occ,
atmlst, max_memory, verbose,
with_k=hybrid)
de2 += ej - hyb * ek # (A,B,dR_A,dR_B)
mem_now = lib.current_memory()[0]
max_memory = max(2000, mf.max_memory*.9-mem_now)
veffa_diag, veffb_diag = _get_vxc_diag(hessobj, mo_coeff, mo_occ, max_memory)
if hybrid and omega != 0:
with mol.with_range_coulomb(omega):
vk1a, vk1b = _get_jk(mol, 'int2e_ipip1', 9, 's2kl',
['jk->s1il', dm0a, 'jk->s1il', dm0b])
veffa_diag -= (alpha-hyb) * vk1a.reshape(3,3,nao,nao)
veffb_diag -= (alpha-hyb) * vk1b.reshape(3,3,nao,nao)
vk1a = vk1b = None
t1 = log.timer_debug1('contracting int2e_ipip1', *t1)
aoslices = mol.aoslice_by_atom()
vxca, vxcb = _get_vxc_deriv2(hessobj, mo_coeff, mo_occ, max_memory)
for i0, ia in enumerate(atmlst):
shl0, shl1, p0, p1 = aoslices[ia]
veffa = vxca[ia]
veffb = vxcb[ia]
shls_slice = (shl0, shl1) + (0, mol.nbas)*3
if hybrid and omega != 0:
with mol.with_range_coulomb(omega):
vk1a, vk1b, vk2a, vk2b = \
_get_jk(mol, 'int2e_ip1ip2', 9, 's1',
['li->s1kj', dm0a[:,p0:p1],
'li->s1kj', dm0b[:,p0:p1],
'lj->s1ki', dm0a ,
'lj->s1ki', dm0b ],
shls_slice=shls_slice)
veffa -= (alpha-hyb) * vk1a.reshape(3,3,nao,nao)
veffb -= (alpha-hyb) * vk1b.reshape(3,3,nao,nao)
veffa[:,:,:,p0:p1] -= (alpha-hyb) * vk2a.reshape(3,3,nao,p1-p0)
veffb[:,:,:,p0:p1] -= (alpha-hyb) * vk2b.reshape(3,3,nao,p1-p0)
t1 = log.timer_debug1('range-separated int2e_ip1ip2 for atom %d'%ia, *t1)
with mol.with_range_coulomb(omega):
vk1a, vk1b = _get_jk(mol, 'int2e_ipvip1', 9, 's2kl',
['li->s1kj', dm0a[:,p0:p1],
'li->s1kj', dm0b[:,p0:p1]],
shls_slice=shls_slice)
veffa -= (alpha-hyb) * vk1a.transpose(0,2,1).reshape(3,3,nao,nao)
veffb -= (alpha-hyb) * vk1b.transpose(0,2,1).reshape(3,3,nao,nao)
t1 = log.timer_debug1('range-separated int2e_ipvip1 for atom %d'%ia, *t1)
vk1a = vk1b = vk2a = vk2b = None
de2[i0,i0] += numpy.einsum('xypq,pq->xy', veffa_diag[:,:,p0:p1], dm0a[p0:p1])*2
de2[i0,i0] += numpy.einsum('xypq,pq->xy', veffb_diag[:,:,p0:p1], dm0b[p0:p1])*2
for j0, ja in enumerate(atmlst[:i0+1]):
q0, q1 = aoslices[ja][2:]
de2[i0,j0] += numpy.einsum('xypq,pq->xy', veffa[:,:,q0:q1], dm0a[q0:q1])*2
de2[i0,j0] += numpy.einsum('xypq,pq->xy', veffb[:,:,q0:q1], dm0b[q0:q1])*2
for j0 in range(i0):
de2[j0,i0] = de2[i0,j0].T
log.timer('UKS partial hessian', *time0)
return de2
[docs]
def make_h1(hessobj, mo_coeff, mo_occ, chkfile=None, atmlst=None, verbose=None):
mol = hessobj.mol
if atmlst is None:
atmlst = range(mol.natm)
nao, nmo = mo_coeff[0].shape
mocca = mo_coeff[0][:,mo_occ[0]>0]
moccb = mo_coeff[1][:,mo_occ[1]>0]
dm0a = numpy.dot(mocca, mocca.T)
dm0b = numpy.dot(moccb, moccb.T)
hcore_deriv = hessobj.base.nuc_grad_method().hcore_generator(mol)
mf = hessobj.base
ni = mf._numint
ni.libxc.test_deriv_order(mf.xc, 2, raise_error=True)
omega, alpha, hyb = ni.rsh_and_hybrid_coeff(mf.xc, spin=mol.spin)
hybrid = ni.libxc.is_hybrid_xc(mf.xc)
mem_now = lib.current_memory()[0]
max_memory = max(2000, mf.max_memory*.9-mem_now)
h1aoa, h1aob = _get_vxc_deriv1(hessobj, mo_coeff, mo_occ, max_memory)
aoslices = mol.aoslice_by_atom()
for i0, ia in enumerate(atmlst):
shl0, shl1, p0, p1 = aoslices[ia]
shls_slice = (shl0, shl1) + (0, mol.nbas)*3
if hybrid:
vj1a, vj1b, vj2a, vj2b, vk1a, vk1b, vk2a, vk2b = \
_get_jk(mol, 'int2e_ip1', 3, 's2kl',
['ji->s2kl', -dm0a[:,p0:p1], 'ji->s2kl', -dm0b[:,p0:p1],
'lk->s1ij', -dm0a , 'lk->s1ij', -dm0b ,
'li->s1kj', -dm0a[:,p0:p1], 'li->s1kj', -dm0b[:,p0:p1],
'jk->s1il', -dm0a , 'jk->s1il', -dm0b ],
shls_slice=shls_slice)
vj1 = vj1a + vj1b
vj2 = vj2a + vj2b
veffa = vj1 - hyb * vk1a
veffb = vj1 - hyb * vk1b
veffa[:,p0:p1] += vj2 - hyb * vk2a
veffb[:,p0:p1] += vj2 - hyb * vk2b
if omega != 0:
with mol.with_range_coulomb(omega):
vk1a, vk1b, vk2a, vk2b = \
_get_jk(mol, 'int2e_ip1', 3, 's2kl',
['li->s1kj', -dm0a[:,p0:p1],
'li->s1kj', -dm0b[:,p0:p1],
'jk->s1il', -dm0a ,
'jk->s1il', -dm0b ],
shls_slice=shls_slice)
veffa -= (alpha-hyb) * vk1a
veffb -= (alpha-hyb) * vk1b
veffa[:,p0:p1] -= (alpha-hyb) * vk2a
veffb[:,p0:p1] -= (alpha-hyb) * vk2b
else:
vj1a, vj1b, vj2a, vj2b = \
_get_jk(mol, 'int2e_ip1', 3, 's2kl',
['ji->s2kl', -dm0a[:,p0:p1], 'ji->s2kl', -dm0b[:,p0:p1],
'lk->s1ij', -dm0a , 'lk->s1ij', -dm0b ],
shls_slice=shls_slice)
vj1 = vj1a + vj1b
vj2 = vj2a + vj2b
veffa = vj1
veffb = vj1.copy()
veffa[:,p0:p1] += vj2
veffb[:,p0:p1] += vj2
h1 = hcore_deriv(ia)
h1aoa[ia] += h1 + veffa + veffa.transpose(0,2,1)
h1aob[ia] += h1 + veffb + veffb.transpose(0,2,1)
if chkfile is None:
return h1aoa, h1aob
else:
for ia in atmlst:
lib.chkfile.save(chkfile, 'scf_f1ao/0/%d'%ia, h1aoa[ia])
lib.chkfile.save(chkfile, 'scf_f1ao/1/%d'%ia, h1aob[ia])
return chkfile
XX, XY, XZ = 4, 5, 6
YX, YY, YZ = 5, 7, 8
ZX, ZY, ZZ = 6, 8, 9
XXX, XXY, XXZ, XYY, XYZ, XZZ = 10, 11, 12, 13, 14, 15
YYY, YYZ, YZZ, ZZZ = 16, 17, 18, 19
def _get_vxc_diag(hessobj, mo_coeff, mo_occ, max_memory):
mol = hessobj.mol
mf = hessobj.base
if hessobj.grids is not None:
grids = hessobj.grids
else:
grids = mf.grids
if grids.coords is None:
grids.build(with_non0tab=True)
nao, nmo = mo_coeff[0].shape
ni = mf._numint
xctype = ni._xc_type(mf.xc)
shls_slice = (0, mol.nbas)
ao_loc = mol.ao_loc_nr()
vmata = numpy.zeros((6,nao,nao))
vmatb = numpy.zeros((6,nao,nao))
if xctype == 'LDA':
ao_deriv = 2
for ao, mask, weight, coords \
in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
rhoa = ni.eval_rho2(mol, ao[0], mo_coeff[0], mo_occ[0], mask, xctype)
rhob = ni.eval_rho2(mol, ao[0], mo_coeff[1], mo_occ[1], mask, xctype)
vxc = ni.eval_xc_eff(mf.xc, (rhoa, rhob), 1, xctype=xctype)[1]
wv = weight * vxc[:,0]
aowa = numint._scale_ao(ao[0], wv[0])
aowb = numint._scale_ao(ao[0], wv[1])
for i in range(6):
vmata[i] += numint._dot_ao_ao(mol, ao[i+4], aowa, mask, shls_slice, ao_loc)
vmatb[i] += numint._dot_ao_ao(mol, ao[i+4], aowb, mask, shls_slice, ao_loc)
aowa = aowb = None
elif xctype == 'GGA':
def contract_(mat, ao, aoidx, wv, mask):
aow = numint._scale_ao(ao[aoidx[0]], wv[1])
aow+= numint._scale_ao(ao[aoidx[1]], wv[2])
aow+= numint._scale_ao(ao[aoidx[2]], wv[3])
mat += numint._dot_ao_ao(mol, aow, ao[0], mask, shls_slice, ao_loc)
ao_deriv = 3
for ao, mask, weight, coords \
in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
rhoa = ni.eval_rho2(mol, ao[:4], mo_coeff[0], mo_occ[0], mask, xctype)
rhob = ni.eval_rho2(mol, ao[:4], mo_coeff[1], mo_occ[1], mask, xctype)
vxc = ni.eval_xc_eff(mf.xc, (rhoa, rhob), 1, xctype=xctype)[1]
wv = weight * vxc
aowa = numint._scale_ao(ao[:4], wv[0,:4])
aowb = numint._scale_ao(ao[:4], wv[1,:4])
for i in range(6):
vmata[i] += numint._dot_ao_ao(mol, ao[i+4], aowa, mask, shls_slice, ao_loc)
vmatb[i] += numint._dot_ao_ao(mol, ao[i+4], aowb, mask, shls_slice, ao_loc)
contract_(vmata[0], ao, [XXX,XXY,XXZ], wv[0], mask)
contract_(vmata[1], ao, [XXY,XYY,XYZ], wv[0], mask)
contract_(vmata[2], ao, [XXZ,XYZ,XZZ], wv[0], mask)
contract_(vmata[3], ao, [XYY,YYY,YYZ], wv[0], mask)
contract_(vmata[4], ao, [XYZ,YYZ,YZZ], wv[0], mask)
contract_(vmata[5], ao, [XZZ,YZZ,ZZZ], wv[0], mask)
contract_(vmatb[0], ao, [XXX,XXY,XXZ], wv[1], mask)
contract_(vmatb[1], ao, [XXY,XYY,XYZ], wv[1], mask)
contract_(vmatb[2], ao, [XXZ,XYZ,XZZ], wv[1], mask)
contract_(vmatb[3], ao, [XYY,YYY,YYZ], wv[1], mask)
contract_(vmatb[4], ao, [XYZ,YYZ,YZZ], wv[1], mask)
contract_(vmatb[5], ao, [XZZ,YZZ,ZZZ], wv[1], mask)
vxc = aowa = aowb = None
elif xctype == 'MGGA':
def contract_(mat, ao, aoidx, wv, mask):
aow = numint._scale_ao(ao[aoidx[0]], wv[1])
aow+= numint._scale_ao(ao[aoidx[1]], wv[2])
aow+= numint._scale_ao(ao[aoidx[2]], wv[3])
mat += numint._dot_ao_ao(mol, aow, ao[0], mask, shls_slice, ao_loc)
ao_deriv = 3
for ao, mask, weight, coords \
in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
rhoa = ni.eval_rho2(mol, ao[:10], mo_coeff[0], mo_occ[0], mask, xctype)
rhob = ni.eval_rho2(mol, ao[:10], mo_coeff[1], mo_occ[1], mask, xctype)
vxc = ni.eval_xc_eff(mf.xc, (rhoa, rhob), 1, xctype=xctype)[1]
wv = weight * vxc
wv[:,4] *= .5 # for the factor 1/2 in tau
aowa = numint._scale_ao(ao[:4], wv[0,:4])
aowb = numint._scale_ao(ao[:4], wv[1,:4])
for i in range(6):
vmata[i] += numint._dot_ao_ao(mol, ao[i+4], aowa, mask, shls_slice, ao_loc)
vmatb[i] += numint._dot_ao_ao(mol, ao[i+4], aowb, mask, shls_slice, ao_loc)
contract_(vmata[0], ao, [XXX,XXY,XXZ], wv[0], mask)
contract_(vmata[1], ao, [XXY,XYY,XYZ], wv[0], mask)
contract_(vmata[2], ao, [XXZ,XYZ,XZZ], wv[0], mask)
contract_(vmata[3], ao, [XYY,YYY,YYZ], wv[0], mask)
contract_(vmata[4], ao, [XYZ,YYZ,YZZ], wv[0], mask)
contract_(vmata[5], ao, [XZZ,YZZ,ZZZ], wv[0], mask)
contract_(vmatb[0], ao, [XXX,XXY,XXZ], wv[1], mask)
contract_(vmatb[1], ao, [XXY,XYY,XYZ], wv[1], mask)
contract_(vmatb[2], ao, [XXZ,XYZ,XZZ], wv[1], mask)
contract_(vmatb[3], ao, [XYY,YYY,YYZ], wv[1], mask)
contract_(vmatb[4], ao, [XYZ,YYZ,YZZ], wv[1], mask)
contract_(vmatb[5], ao, [XZZ,YZZ,ZZZ], wv[1], mask)
aowa = [numint._scale_ao(ao[i], wv[0,4]) for i in range(1, 4)]
aowb = [numint._scale_ao(ao[i], wv[1,4]) for i in range(1, 4)]
for i, j in enumerate([XXX, XXY, XXZ, XYY, XYZ, XZZ]):
vmata[i] += numint._dot_ao_ao(mol, ao[j], aowa[0], mask, shls_slice, ao_loc)
vmatb[i] += numint._dot_ao_ao(mol, ao[j], aowb[0], mask, shls_slice, ao_loc)
for i, j in enumerate([XXY, XYY, XYZ, YYY, YYZ, YZZ]):
vmata[i] += numint._dot_ao_ao(mol, ao[j], aowa[1], mask, shls_slice, ao_loc)
vmatb[i] += numint._dot_ao_ao(mol, ao[j], aowb[1], mask, shls_slice, ao_loc)
for i, j in enumerate([XXZ, XYZ, XZZ, YYZ, YZZ, ZZZ]):
vmata[i] += numint._dot_ao_ao(mol, ao[j], aowa[2], mask, shls_slice, ao_loc)
vmatb[i] += numint._dot_ao_ao(mol, ao[j], aowb[2], mask, shls_slice, ao_loc)
vmata = vmata[[0,1,2, 1,3,4, 2,4,5]].reshape(3,3,nao,nao)
vmatb = vmatb[[0,1,2, 1,3,4, 2,4,5]].reshape(3,3,nao,nao)
return vmata, vmatb
def _get_vxc_deriv2(hessobj, mo_coeff, mo_occ, max_memory):
mol = hessobj.mol
mf = hessobj.base
if hessobj.grids is not None:
grids = hessobj.grids
else:
grids = mf.grids
if grids.coords is None:
grids.build(with_non0tab=True)
nao, nmo = mo_coeff[0].shape
ni = mf._numint
xctype = ni._xc_type(mf.xc)
aoslices = mol.aoslice_by_atom()
shls_slice = (0, mol.nbas)
ao_loc = mol.ao_loc_nr()
dm0a, dm0b = mf.make_rdm1(mo_coeff, mo_occ)
vmata = numpy.zeros((mol.natm,3,3,nao,nao))
vmatb = numpy.zeros((mol.natm,3,3,nao,nao))
ipipa = numpy.zeros((3,3,nao,nao))
ipipb = numpy.zeros((3,3,nao,nao))
if xctype == 'LDA':
ao_deriv = 1
for ao, mask, weight, coords \
in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
rhoa = ni.eval_rho2(mol, ao[0], mo_coeff[0], mo_occ[0], mask, xctype)
rhob = ni.eval_rho2(mol, ao[0], mo_coeff[1], mo_occ[1], mask, xctype)
vxc, fxc = ni.eval_xc_eff(mf.xc, (rhoa, rhob), 2, xctype=xctype)[1:3]
wv = weight * vxc[:,0]
aow = numpy.einsum('xpi,p->xpi', ao[1:4], wv[0])
rks_hess._d1d2_dot_(ipipa, mol, aow, ao[1:4], mask, ao_loc, False)
aow = numpy.einsum('xpi,p->xpi', ao[1:4], wv[1])
rks_hess._d1d2_dot_(ipipb, mol, aow, ao[1:4], mask, ao_loc, False)
ao_dm0a = numint._dot_ao_dm(mol, ao[0], dm0a, mask, shls_slice, ao_loc)
ao_dm0b = numint._dot_ao_dm(mol, ao[0], dm0b, mask, shls_slice, ao_loc)
wf = weight * fxc[:,0,:,0]
for ia in range(mol.natm):
p0, p1 = aoslices[ia][2:]
# *2 for \nabla|ket> in rho1
rho1a = numpy.einsum('xpi,pi->xp', ao[1:,:,p0:p1], ao_dm0a[:,p0:p1]) * 2
rho1b = numpy.einsum('xpi,pi->xp', ao[1:,:,p0:p1], ao_dm0b[:,p0:p1]) * 2
wv = wf[0,:,None] * rho1a
wv += wf[1,:,None] * rho1b
# aow ~ rho1 ~ d/dR1
aow = numpy.einsum('pi,xp->xpi', ao[0], wv[0])
rks_hess._d1d2_dot_(vmata[ia], mol, ao[1:4], aow, mask, ao_loc, False)
aow = numpy.einsum('pi,xp->xpi', ao[0], wv[1])
rks_hess._d1d2_dot_(vmatb[ia], mol, ao[1:4], aow, mask, ao_loc, False)
ao_dm0a = ao_dm0b = aow = None
for ia in range(mol.natm):
p0, p1 = aoslices[ia][2:]
vmata[ia,:,:,:,p0:p1] += ipipa[:,:,:,p0:p1]
vmatb[ia,:,:,:,p0:p1] += ipipb[:,:,:,p0:p1]
elif xctype == 'GGA':
ao_deriv = 2
for ao, mask, weight, coords \
in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
rhoa = ni.eval_rho2(mol, ao[:4], mo_coeff[0], mo_occ[0], mask, xctype)
rhob = ni.eval_rho2(mol, ao[:4], mo_coeff[1], mo_occ[1], mask, xctype)
vxc, fxc = ni.eval_xc_eff(mf.xc, (rhoa, rhob), 2, xctype=xctype)[1:3]
wv = weight * vxc
wv[:,0] *= .5
aow = rks_grad._make_dR_dao_w(ao, wv[0])
rks_hess._d1d2_dot_(ipipa, mol, aow, ao[1:4], mask, ao_loc, False)
aow = rks_grad._make_dR_dao_w(ao, wv[1])
rks_hess._d1d2_dot_(ipipb, mol, aow, ao[1:4], mask, ao_loc, False)
ao_dm0a = [numint._dot_ao_dm(mol, ao[i], dm0a, mask, shls_slice, ao_loc) for i in range(4)]
ao_dm0b = [numint._dot_ao_dm(mol, ao[i], dm0b, mask, shls_slice, ao_loc) for i in range(4)]
wf = weight * fxc
for ia in range(mol.natm):
dR_rho1a = rks_hess._make_dR_rho1(ao, ao_dm0a, ia, aoslices, xctype)
dR_rho1b = rks_hess._make_dR_rho1(ao, ao_dm0b, ia, aoslices, xctype)
wv = numpy.einsum('xbyg,sxg->bsyg', wf[0], dR_rho1a)
wv += numpy.einsum('xbyg,sxg->bsyg', wf[1], dR_rho1b)
wv[:,:,0] *= .5
wva, wvb = wv
aow = rks_grad._make_dR_dao_w(ao, wva[0])
rks_grad._d1_dot_(vmata[ia,0], mol, aow, ao[0], mask, ao_loc, True)
aow = rks_grad._make_dR_dao_w(ao, wva[1])
rks_grad._d1_dot_(vmata[ia,1], mol, aow, ao[0], mask, ao_loc, True)
aow = rks_grad._make_dR_dao_w(ao, wva[2])
rks_grad._d1_dot_(vmata[ia,2], mol, aow, ao[0], mask, ao_loc, True)
aow = [numint._scale_ao(ao[:4], wva[i,:4]) for i in range(3)]
rks_hess._d1d2_dot_(vmata[ia], mol, ao[1:4], aow, mask, ao_loc, False)
aow = rks_grad._make_dR_dao_w(ao, wvb[0])
rks_grad._d1_dot_(vmatb[ia,0], mol, aow, ao[0], mask, ao_loc, True)
aow = rks_grad._make_dR_dao_w(ao, wvb[1])
rks_grad._d1_dot_(vmatb[ia,1], mol, aow, ao[0], mask, ao_loc, True)
aow = rks_grad._make_dR_dao_w(ao, wvb[2])
rks_grad._d1_dot_(vmatb[ia,2], mol, aow, ao[0], mask, ao_loc, True)
aow = [numint._scale_ao(ao[:4], wvb[i,:4]) for i in range(3)]
rks_hess._d1d2_dot_(vmatb[ia], mol, ao[1:4], aow, mask, ao_loc, False)
ao_dm0a = ao_dm0b = aow = None
for ia in range(mol.natm):
p0, p1 = aoslices[ia][2:]
vmata[ia,:,:,:,p0:p1] += ipipa[:,:,:,p0:p1]
vmata[ia,:,:,:,p0:p1] += ipipa[:,:,p0:p1].transpose(1,0,3,2)
vmatb[ia,:,:,:,p0:p1] += ipipb[:,:,:,p0:p1]
vmatb[ia,:,:,:,p0:p1] += ipipb[:,:,p0:p1].transpose(1,0,3,2)
elif xctype == 'MGGA':
XX, XY, XZ = 4, 5, 6
YX, YY, YZ = 5, 7, 8
ZX, ZY, ZZ = 6, 8, 9
ao_deriv = 2
for ao, mask, weight, coords \
in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
rhoa = ni.eval_rho2(mol, ao[:10], mo_coeff[0], mo_occ[0], mask, xctype)
rhob = ni.eval_rho2(mol, ao[:10], mo_coeff[1], mo_occ[1], mask, xctype)
vxc, fxc = ni.eval_xc_eff(mf.xc, (rhoa, rhob), 2, xctype=xctype)[1:3]
wv = weight * vxc
wv[:,0] *= .5
wv[:,4] *= .25
aow = rks_grad._make_dR_dao_w(ao, wv[0])
rks_hess._d1d2_dot_(ipipa, mol, aow, ao[1:4], mask, ao_loc, False)
aow = rks_grad._make_dR_dao_w(ao, wv[1])
rks_hess._d1d2_dot_(ipipb, mol, aow, ao[1:4], mask, ao_loc, False)
aow = [numint._scale_ao(ao[i], wv[0,4]) for i in range(4, 10)]
rks_hess._d1d2_dot_(ipipa, mol, [aow[0], aow[1], aow[2]], [ao[XX], ao[XY], ao[XZ]], mask, ao_loc, False)
rks_hess._d1d2_dot_(ipipa, mol, [aow[1], aow[3], aow[4]], [ao[YX], ao[YY], ao[YZ]], mask, ao_loc, False)
rks_hess._d1d2_dot_(ipipa, mol, [aow[2], aow[4], aow[5]], [ao[ZX], ao[ZY], ao[ZZ]], mask, ao_loc, False)
aow = [numint._scale_ao(ao[i], wv[1,4]) for i in range(4, 10)]
rks_hess._d1d2_dot_(ipipb, mol, [aow[0], aow[1], aow[2]], [ao[XX], ao[XY], ao[XZ]], mask, ao_loc, False)
rks_hess._d1d2_dot_(ipipb, mol, [aow[1], aow[3], aow[4]], [ao[YX], ao[YY], ao[YZ]], mask, ao_loc, False)
rks_hess._d1d2_dot_(ipipb, mol, [aow[2], aow[4], aow[5]], [ao[ZX], ao[ZY], ao[ZZ]], mask, ao_loc, False)
ao_dm0a = [numint._dot_ao_dm(mol, ao[i], dm0a, mask, shls_slice, ao_loc) for i in range(4)]
ao_dm0b = [numint._dot_ao_dm(mol, ao[i], dm0b, mask, shls_slice, ao_loc) for i in range(4)]
wf = weight * fxc
for ia in range(mol.natm):
dR_rho1a = rks_hess._make_dR_rho1(ao, ao_dm0a, ia, aoslices, xctype)
dR_rho1b = rks_hess._make_dR_rho1(ao, ao_dm0b, ia, aoslices, xctype)
wv = numpy.einsum('xbyg,sxg->bsyg', wf[0], dR_rho1a)
wv += numpy.einsum('xbyg,sxg->bsyg', wf[1], dR_rho1b)
wv[:,:,0] *= .5
wv[:,:,4] *= .5
wva, wvb = wv
aow = rks_grad._make_dR_dao_w(ao, wva[0])
rks_grad._d1_dot_(vmata[ia,0], mol, aow, ao[0], mask, ao_loc, True)
aow = rks_grad._make_dR_dao_w(ao, wva[1])
rks_grad._d1_dot_(vmata[ia,1], mol, aow, ao[0], mask, ao_loc, True)
aow = rks_grad._make_dR_dao_w(ao, wva[2])
rks_grad._d1_dot_(vmata[ia,2], mol, aow, ao[0], mask, ao_loc, True)
aow = [numint._scale_ao(ao[:4], wva[i,:4]) for i in range(3)]
rks_hess._d1d2_dot_(vmata[ia], mol, ao[1:4], aow, mask, ao_loc, False)
aow = rks_grad._make_dR_dao_w(ao, wvb[0])
rks_grad._d1_dot_(vmatb[ia,0], mol, aow, ao[0], mask, ao_loc, True)
aow = rks_grad._make_dR_dao_w(ao, wvb[1])
rks_grad._d1_dot_(vmatb[ia,1], mol, aow, ao[0], mask, ao_loc, True)
aow = rks_grad._make_dR_dao_w(ao, wvb[2])
rks_grad._d1_dot_(vmatb[ia,2], mol, aow, ao[0], mask, ao_loc, True)
aow = [numint._scale_ao(ao[:4], wvb[i,:4]) for i in range(3)]
rks_hess._d1d2_dot_(vmatb[ia], mol, ao[1:4], aow, mask, ao_loc, False)
aow = [numint._scale_ao(ao[1], wva[i,4]) for i in range(3)]
rks_hess._d1d2_dot_(vmata[ia], mol, [ao[XX], ao[XY], ao[XZ]], aow, mask, ao_loc, False)
aow = [numint._scale_ao(ao[2], wva[i,4]) for i in range(3)]
rks_hess._d1d2_dot_(vmata[ia], mol, [ao[YX], ao[YY], ao[YZ]], aow, mask, ao_loc, False)
aow = [numint._scale_ao(ao[3], wva[i,4]) for i in range(3)]
rks_hess._d1d2_dot_(vmata[ia], mol, [ao[ZX], ao[ZY], ao[ZZ]], aow, mask, ao_loc, False)
aow = [numint._scale_ao(ao[1], wvb[i,4]) for i in range(3)]
rks_hess._d1d2_dot_(vmatb[ia], mol, [ao[XX], ao[XY], ao[XZ]], aow, mask, ao_loc, False)
aow = [numint._scale_ao(ao[2], wvb[i,4]) for i in range(3)]
rks_hess._d1d2_dot_(vmatb[ia], mol, [ao[YX], ao[YY], ao[YZ]], aow, mask, ao_loc, False)
aow = [numint._scale_ao(ao[3], wvb[i,4]) for i in range(3)]
rks_hess._d1d2_dot_(vmatb[ia], mol, [ao[ZX], ao[ZY], ao[ZZ]], aow, mask, ao_loc, False)
for ia in range(mol.natm):
p0, p1 = aoslices[ia][2:]
vmata[ia,:,:,:,p0:p1] += ipipa[:,:,:,p0:p1]
vmata[ia,:,:,:,p0:p1] += ipipa[:,:,p0:p1].transpose(1,0,3,2)
vmatb[ia,:,:,:,p0:p1] += ipipb[:,:,:,p0:p1]
vmatb[ia,:,:,:,p0:p1] += ipipb[:,:,p0:p1].transpose(1,0,3,2)
return vmata, vmatb
def _get_vxc_deriv1(hessobj, mo_coeff, mo_occ, max_memory):
mol = hessobj.mol
mf = hessobj.base
if hessobj.grids is not None:
grids = hessobj.grids
else:
grids = mf.grids
if grids.coords is None:
grids.build(with_non0tab=True)
nao, nmo = mo_coeff[0].shape
ni = mf._numint
xctype = ni._xc_type(mf.xc)
aoslices = mol.aoslice_by_atom()
shls_slice = (0, mol.nbas)
ao_loc = mol.ao_loc_nr()
dm0a, dm0b = mf.make_rdm1(mo_coeff, mo_occ)
vmata = numpy.zeros((mol.natm,3,nao,nao))
vmatb = numpy.zeros((mol.natm,3,nao,nao))
max_memory = max(2000, max_memory-(vmata.size+vmatb.size)*8/1e6)
if xctype == 'LDA':
ao_deriv = 1
for ao, mask, weight, coords \
in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
rhoa = ni.eval_rho2(mol, ao[0], mo_coeff[0], mo_occ[0], mask, xctype)
rhob = ni.eval_rho2(mol, ao[0], mo_coeff[1], mo_occ[1], mask, xctype)
vxc, fxc = ni.eval_xc_eff(mf.xc, (rhoa, rhob), 2, xctype=xctype)[1:3]
wv = weight * vxc[:,0]
ao_dm0a = numint._dot_ao_dm(mol, ao[0], dm0a, mask, shls_slice, ao_loc)
ao_dm0b = numint._dot_ao_dm(mol, ao[0], dm0b, mask, shls_slice, ao_loc)
aow1a = numpy.einsum('xpi,p->xpi', ao[1:], wv[0])
aow1b = numpy.einsum('xpi,p->xpi', ao[1:], wv[1])
wf = weight * fxc[:,0,:,0]
for ia in range(mol.natm):
p0, p1 = aoslices[ia][2:]
# First order density = rho1 * 2. *2 is not applied because + c.c. in the end
rho1a = numpy.einsum('xpi,pi->xp', ao[1:,:,p0:p1], ao_dm0a[:,p0:p1])
rho1b = numpy.einsum('xpi,pi->xp', ao[1:,:,p0:p1], ao_dm0b[:,p0:p1])
wv = wf[0,:,None] * rho1a
wv += wf[1,:,None] * rho1b
aow = numpy.einsum('pi,xp->xpi', ao[0], wv[0])
aow[:,:,p0:p1] += aow1a[:,:,p0:p1]
rks_grad._d1_dot_(vmata[ia], mol, aow, ao[0], mask, ao_loc, True)
aow = numpy.einsum('pi,xp->xpi', ao[0], wv[1])
aow[:,:,p0:p1] += aow1b[:,:,p0:p1]
rks_grad._d1_dot_(vmatb[ia], mol, aow, ao[0], mask, ao_loc, True)
ao_dm0a = ao_dm0b = aow = aow1a = aow1b = None
for ia in range(mol.natm):
vmata[ia] = -vmata[ia] - vmata[ia].transpose(0,2,1)
vmatb[ia] = -vmatb[ia] - vmatb[ia].transpose(0,2,1)
elif xctype == 'GGA':
ao_deriv = 2
vipa = numpy.zeros((3,nao,nao))
vipb = numpy.zeros((3,nao,nao))
for ao, mask, weight, coords \
in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
rhoa = ni.eval_rho2(mol, ao[:4], mo_coeff[0], mo_occ[0], mask, xctype)
rhob = ni.eval_rho2(mol, ao[:4], mo_coeff[1], mo_occ[1], mask, xctype)
vxc, fxc = ni.eval_xc_eff(mf.xc, (rhoa, rhob), 2, xctype=xctype)[1:3]
wv = weight * vxc
wv[:,0] *= .5
rks_grad._gga_grad_sum_(vipa, mol, ao, wv[0], mask, ao_loc)
rks_grad._gga_grad_sum_(vipb, mol, ao, wv[1], mask, ao_loc)
ao_dm0a = [numint._dot_ao_dm(mol, ao[i], dm0a, mask, shls_slice, ao_loc) for i in range(4)]
ao_dm0b = [numint._dot_ao_dm(mol, ao[i], dm0b, mask, shls_slice, ao_loc) for i in range(4)]
wf = weight * fxc
for ia in range(mol.natm):
dR_rho1a = rks_hess._make_dR_rho1(ao, ao_dm0a, ia, aoslices, xctype)
dR_rho1b = rks_hess._make_dR_rho1(ao, ao_dm0b, ia, aoslices, xctype)
wv = numpy.einsum('xbyg,sxg->bsyg', wf[0], dR_rho1a)
wv += numpy.einsum('xbyg,sxg->bsyg', wf[1], dR_rho1b)
wv[:,:,0] *= .5
wva, wvb = wv
aow = [numint._scale_ao(ao[:4], wva[i,:4]) for i in range(3)]
rks_grad._d1_dot_(vmata[ia], mol, aow, ao[0], mask, ao_loc, True)
aow = [numint._scale_ao(ao[:4], wvb[i,:4]) for i in range(3)]
rks_grad._d1_dot_(vmatb[ia], mol, aow, ao[0], mask, ao_loc, True)
ao_dm0a = ao_dm0b = aow = None
for ia in range(mol.natm):
p0, p1 = aoslices[ia][2:]
vmata[ia,:,p0:p1] += vipa[:,p0:p1]
vmatb[ia,:,p0:p1] += vipb[:,p0:p1]
vmata[ia] = -vmata[ia] - vmata[ia].transpose(0,2,1)
vmatb[ia] = -vmatb[ia] - vmatb[ia].transpose(0,2,1)
elif xctype == 'MGGA':
if grids.level < 5:
logger.warn(mol, 'MGGA Hessian is sensitive to dft grids.')
ao_deriv = 2
vipa = numpy.zeros((3,nao,nao))
vipb = numpy.zeros((3,nao,nao))
for ao, mask, weight, coords \
in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
rhoa = ni.eval_rho2(mol, ao[:10], mo_coeff[0], mo_occ[0], mask, xctype)
rhob = ni.eval_rho2(mol, ao[:10], mo_coeff[1], mo_occ[1], mask, xctype)
vxc, fxc = ni.eval_xc_eff(mf.xc, (rhoa, rhob), 2, xctype=xctype)[1:3]
wv = weight * vxc
wv[:,0] *= .5
wv[:,4] *= .5
rks_grad._gga_grad_sum_(vipa, mol, ao, wv[0], mask, ao_loc)
rks_grad._gga_grad_sum_(vipb, mol, ao, wv[1], mask, ao_loc)
rks_grad._tau_grad_dot_(vipa, mol, ao, wv[0,4], mask, ao_loc, True)
rks_grad._tau_grad_dot_(vipb, mol, ao, wv[1,4], mask, ao_loc, True)
ao_dm0a = [numint._dot_ao_dm(mol, ao[i], dm0a, mask, shls_slice, ao_loc) for i in range(4)]
ao_dm0b = [numint._dot_ao_dm(mol, ao[i], dm0b, mask, shls_slice, ao_loc) for i in range(4)]
wf = weight * fxc
for ia in range(mol.natm):
dR_rho1a = rks_hess._make_dR_rho1(ao, ao_dm0a, ia, aoslices, xctype)
dR_rho1b = rks_hess._make_dR_rho1(ao, ao_dm0b, ia, aoslices, xctype)
wv = numpy.einsum('xbyg,sxg->bsyg', wf[0], dR_rho1a)
wv += numpy.einsum('xbyg,sxg->bsyg', wf[1], dR_rho1b)
wv[:,:,0] *= .5
wv[:,:,4] *= .25
wva, wvb = wv
aow = [numint._scale_ao(ao[:4], wva[i,:4]) for i in range(3)]
rks_grad._d1_dot_(vmata[ia], mol, aow, ao[0], mask, ao_loc, True)
aow = [numint._scale_ao(ao[:4], wvb[i,:4]) for i in range(3)]
rks_grad._d1_dot_(vmatb[ia], mol, aow, ao[0], mask, ao_loc, True)
for j in range(1, 4):
aow = [numint._scale_ao(ao[j], wva[i,4]) for i in range(3)]
rks_grad._d1_dot_(vmata[ia], mol, aow, ao[j], mask, ao_loc, True)
aow = [numint._scale_ao(ao[j], wvb[i,4]) for i in range(3)]
rks_grad._d1_dot_(vmatb[ia], mol, aow, ao[j], mask, ao_loc, True)
for ia in range(mol.natm):
p0, p1 = aoslices[ia][2:]
vmata[ia,:,p0:p1] += vipa[:,p0:p1]
vmatb[ia,:,p0:p1] += vipb[:,p0:p1]
vmata[ia] = -vmata[ia] - vmata[ia].transpose(0,2,1)
vmatb[ia] = -vmatb[ia] - vmatb[ia].transpose(0,2,1)
return vmata, vmatb
[docs]
class Hessian(uhf_hess.Hessian):
'''Non-relativistic UKS hessian'''
_keys = set(['grids', 'grid_response'])
def __init__(self, mf):
uhf_hess.Hessian.__init__(self, mf)
self.grids = None
self.grid_response = False
partial_hess_elec = partial_hess_elec
make_h1 = make_h1
from pyscf import dft
dft.uks.UKS.Hessian = dft.uks_symm.UKS.Hessian = lib.class_as_method(Hessian)
if __name__ == '__main__':
from pyscf import gto
from pyscf import dft
#xc_code = 'lda,vwn'
xc_code = 'wb97x'
#xc_code = 'b3lyp'
mol = gto.Mole()
mol.verbose = 0
mol.output = None
mol.atom = [
[1 , (1. , 0. , 0.000)],
[1 , (0. , 1. , 0.000)],
[1 , (0. , -1.517 , 1.177)],
[1 , (0. , 1.517 , 1.177)] ]
mol.basis = '631g'
mol.unit = 'B'
mol.build()
mf = dft.UKS(mol)
mf.xc = xc_code
mf.conv_tol = 1e-14
mf.kernel()
n3 = mol.natm * 3
hobj = mf.Hessian()
e2 = hobj.kernel().transpose(0,2,1,3).reshape(n3,n3)
print(lib.fp(e2) - -0.42286407986042956)
print(lib.fp(e2) - -0.45453541215680582)
print(lib.fp(e2) - -0.41385170026016327)
mol.spin = 2
mf = dft.UKS(mol)
mf.conv_tol = 1e-14
mf.xc = xc_code
mf.scf()
n3 = mol.natm * 3
hobj = Hessian(mf)
e2 = hobj.kernel().transpose(0,2,1,3).reshape(n3,n3)
def grad_full(ia, inc):
coord = mol.atom_coord(ia).copy()
ptr = mol._atm[ia,gto.PTR_COORD]
de = []
for i in range(3):
mol._env[ptr+i] = coord[i] + inc
mf = dft.UKS(mol).run(conv_tol=1e-14, xc=xc_code).run()
e1a = mf.nuc_grad_method().set(grid_response=True).kernel()
mol._env[ptr+i] = coord[i] - inc
mf = dft.UKS(mol).run(conv_tol=1e-14, xc=xc_code).run()
e1b = mf.nuc_grad_method().set(grid_response=True).kernel()
mol._env[ptr+i] = coord[i]
de.append((e1a-e1b)/(2*inc))
return de
e2ref = [grad_full(ia, .5e-3) for ia in range(mol.natm)]
e2ref = numpy.asarray(e2ref).reshape(n3,n3)
print(numpy.linalg.norm(e2-e2ref))
print(abs(e2-e2ref).max())
print(numpy.allclose(e2,e2ref,atol=1e-4))
# \partial^2 E / \partial R \partial R'
e2 = hobj.partial_hess_elec(mf.mo_energy, mf.mo_coeff, mf.mo_occ)
e2 += hobj.hess_nuc(mol)
e2 = e2.transpose(0,2,1,3).reshape(n3,n3)
def grad_partial_R(ia, inc):
coord = mol.atom_coord(ia).copy()
ptr = mol._atm[ia,gto.PTR_COORD]
de = []
for i in range(3):
mol._env[ptr+i] = coord[i] + inc
e1a = mf.nuc_grad_method().kernel()
mol._env[ptr+i] = coord[i] - inc
e1b = mf.nuc_grad_method().kernel()
mol._env[ptr+i] = coord[i]
de.append((e1a-e1b)/(2*inc))
return de
e2ref = [grad_partial_R(ia, .5e-4) for ia in range(mol.natm)]
e2ref = numpy.asarray(e2ref).reshape(n3,n3)
print(numpy.linalg.norm(e2-e2ref))
print(abs(e2-e2ref).max())
print(numpy.allclose(e2,e2ref,atol=1e-6))