#!/usr/bin/env python
# Copyright 2014-2019 The PySCF Developers. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Author: Qiming Sun <osirpt.sun@gmail.com>
#
import sys
from functools import reduce
import numpy
import scipy.linalg
from pyscf import gto
from pyscf import lib
from pyscf.lib import logger
from pyscf.mcscf import casci
from pyscf.mcscf.casci import CASCI, get_fock, cas_natorb, canonicalize
from pyscf.mcscf import mc_ao2mo
from pyscf.mcscf import chkfile
from pyscf import ao2mo
from pyscf import scf
from pyscf.soscf import ciah
from pyscf import __config__
WITH_MICRO_SCHEDULER = getattr(__config__, 'mcscf_mc1step_CASSCF_with_micro_scheduler', False)
WITH_STEPSIZE_SCHEDULER = getattr(__config__, 'mcscf_mc1step_CASSCF_with_stepsize_scheduler', True)
# ref. JCP, 82, 5053 (1985); DOI: 10.1063/1.448627 and JCP 73, 2342 (1980); DOI:10.1063/1.440384
# gradients, hessian operator and hessian diagonal
[docs]
def gen_g_hop(casscf, mo, u, casdm1, casdm2, eris):
ncas = casscf.ncas
nelecas = casscf.nelecas
ncore = casscf.ncore
nocc = ncas + ncore
nmo = mo.shape[1]
dm1 = numpy.zeros((nmo,nmo))
idx = numpy.arange(ncore)
dm1[idx,idx] = 2
dm1[ncore:nocc,ncore:nocc] = casdm1
# part5
jkcaa = numpy.empty((nocc,ncas))
# part2, part3
vhf_a = numpy.empty((nmo,nmo))
# part1 ~ (J + 2K)
dm2tmp = casdm2.transpose(1,2,0,3) + casdm2.transpose(0,2,1,3)
dm2tmp = dm2tmp.reshape(ncas**2,-1)
hdm2 = numpy.empty((nmo,ncas,nmo,ncas))
g_dm2 = numpy.empty((nmo,ncas))
for i in range(nmo):
jbuf = eris.ppaa[i]
kbuf = eris.papa[i]
if i < nocc:
jkcaa[i] = numpy.einsum('ik,ik->i', 6*kbuf[:,i]-2*jbuf[i], casdm1)
vhf_a[i] =(numpy.einsum('quv,uv->q', jbuf, casdm1) -
numpy.einsum('uqv,uv->q', kbuf, casdm1) * .5)
jtmp = lib.dot(jbuf.reshape(nmo,-1), casdm2.reshape(ncas*ncas,-1))
jtmp = jtmp.reshape(nmo,ncas,ncas)
ktmp = lib.dot(kbuf.transpose(1,0,2).reshape(nmo,-1), dm2tmp)
hdm2[i] = (ktmp.reshape(nmo,ncas,ncas)+jtmp).transpose(1,0,2)
g_dm2[i] = numpy.einsum('uuv->v', jtmp[ncore:nocc])
jbuf = kbuf = jtmp = ktmp = dm2tmp = None
vhf_ca = eris.vhf_c + vhf_a
h1e_mo = reduce(numpy.dot, (mo.T, casscf.get_hcore(), mo))
################# gradient #################
g = numpy.zeros_like(h1e_mo)
g[:,:ncore] = (h1e_mo[:,:ncore] + vhf_ca[:,:ncore]) * 2
g[:,ncore:nocc] = numpy.dot(h1e_mo[:,ncore:nocc]+eris.vhf_c[:,ncore:nocc],casdm1)
g[:,ncore:nocc] += g_dm2
def gorb_update(u, fcivec):
uc = u[:,:ncore].copy()
ua = u[:,ncore:nocc].copy()
rmat = u - numpy.eye(nmo)
ra = rmat[:,ncore:nocc].copy()
mo1 = numpy.dot(mo, u)
mo_c = numpy.dot(mo, uc)
mo_a = numpy.dot(mo, ua)
dm_c = numpy.dot(mo_c, mo_c.T) * 2
casdm1, casdm2 = casscf.fcisolver.make_rdm12(fcivec, ncas, nelecas)
dm_a = reduce(numpy.dot, (mo_a, casdm1, mo_a.T))
vj, vk = casscf.get_jk(casscf.mol, (dm_c, dm_a))
vhf_c = reduce(numpy.dot, (mo1.T, vj[0]-vk[0]*.5, mo1[:,:nocc]))
vhf_a = reduce(numpy.dot, (mo1.T, vj[1]-vk[1]*.5, mo1[:,:nocc]))
h1e_mo1 = reduce(numpy.dot, (u.T, h1e_mo, u[:,:nocc]))
p1aa = numpy.empty((nmo,ncas,ncas*ncas))
paa1 = numpy.empty((nmo,ncas*ncas,ncas))
aaaa = numpy.empty([ncas]*4)
for i in range(nmo):
jbuf = eris.ppaa[i]
kbuf = eris.papa[i]
p1aa[i] = lib.dot(ua.T, jbuf.reshape(nmo,-1))
paa1[i] = lib.dot(kbuf.transpose(0,2,1).reshape(-1,nmo), ra)
if ncore <= i < nocc:
aaaa[i-ncore] = jbuf[ncore:nocc]
g = numpy.zeros_like(h1e_mo)
g[:,:ncore] = (h1e_mo1[:,:ncore] + vhf_c[:,:ncore] + vhf_a[:,:ncore]) * 2
g[:,ncore:nocc] = numpy.dot(h1e_mo1[:,ncore:nocc]+vhf_c[:,ncore:nocc], casdm1)
# 0000 + 1000 + 0100 + 0010 + 0001 + 1100 + 1010 + 1001 (missing 0110 + 0101 + 0011)
p1aa = lib.dot(u.T, p1aa.reshape(nmo,-1)).reshape(nmo,ncas,ncas,ncas)
paa1 = lib.dot(u.T, paa1.reshape(nmo,-1)).reshape(nmo,ncas,ncas,ncas)
p1aa += paa1
p1aa += paa1.transpose(0,1,3,2)
g[:,ncore:nocc] += numpy.einsum('puwx,wxuv->pv', p1aa, casdm2)
return casscf.pack_uniq_var(g-g.T)
############## hessian, diagonal ###########
# part7
h_diag = numpy.einsum('ii,jj->ij', h1e_mo, dm1) - h1e_mo * dm1
h_diag = h_diag + h_diag.T
# part8
g_diag = g.diagonal()
h_diag -= g_diag + g_diag.reshape(-1,1)
idx = numpy.arange(nmo)
h_diag[idx,idx] += g_diag * 2
# part2, part3
v_diag = vhf_ca.diagonal() # (pr|kl) * E(sq,lk)
h_diag[:,:ncore] += v_diag.reshape(-1,1) * 2
h_diag[:ncore] += v_diag * 2
idx = numpy.arange(ncore)
h_diag[idx,idx] -= v_diag[:ncore] * 4
# V_{pr} E_{sq}
tmp = numpy.einsum('ii,jj->ij', eris.vhf_c, casdm1)
h_diag[:,ncore:nocc] += tmp
h_diag[ncore:nocc,:] += tmp.T
tmp = -eris.vhf_c[ncore:nocc,ncore:nocc] * casdm1
h_diag[ncore:nocc,ncore:nocc] += tmp + tmp.T
# part4
# -2(pr|sq) + 4(pq|sr) + 4(pq|rs) - 2(ps|rq)
tmp = 6 * eris.k_pc - 2 * eris.j_pc
h_diag[ncore:,:ncore] += tmp[ncore:]
h_diag[:ncore,ncore:] += tmp[ncore:].T
# part5 and part6 diag
# -(qr|kp) E_s^k p in core, sk in active
h_diag[:nocc,ncore:nocc] -= jkcaa
h_diag[ncore:nocc,:nocc] -= jkcaa.T
v_diag = numpy.einsum('ijij->ij', hdm2)
h_diag[ncore:nocc,:] += v_diag.T
h_diag[:,ncore:nocc] += v_diag
# Does this term contribute to internal rotation?
# h_diag[ncore:nocc,ncore:nocc] -= v_diag[:,ncore:nocc]*2
g_orb = casscf.pack_uniq_var(g-g.T)
h_diag = casscf.pack_uniq_var(h_diag)
def h_op(x):
x1 = casscf.unpack_uniq_var(x)
# part7
# (-h_{sp} R_{rs} gamma_{rq} - h_{rq} R_{pq} gamma_{sp})/2 + (pr<->qs)
x2 = reduce(lib.dot, (h1e_mo, x1, dm1))
# part8
# (g_{ps}\delta_{qr}R_rs + g_{qr}\delta_{ps}) * R_pq)/2 + (pr<->qs)
x2 -= numpy.dot((g+g.T), x1) * .5
# part2
# (-2Vhf_{sp}\delta_{qr}R_pq - 2Vhf_{qr}\delta_{sp}R_rs)/2 + (pr<->qs)
x2[:ncore] += reduce(numpy.dot, (x1[:ncore,ncore:], vhf_ca[ncore:])) * 2
# part3
# (-Vhf_{sp}gamma_{qr}R_{pq} - Vhf_{qr}gamma_{sp}R_{rs})/2 + (pr<->qs)
x2[ncore:nocc] += reduce(numpy.dot, (casdm1, x1[ncore:nocc], eris.vhf_c))
# part1
x2[:,ncore:nocc] += numpy.einsum('purv,rv->pu', hdm2, x1[:,ncore:nocc])
# part4, part5, part6
if ncore > 0:
# Due to x1_rs [4(pq|sr) + 4(pq|rs) - 2(pr|sq) - 2(ps|rq)] for r>s p>q,
# == -x1_sr [4(pq|sr) + 4(pq|rs) - 2(pr|sq) - 2(ps|rq)] for r>s p>q,
# x2[:,:ncore] += H * x1[:,:ncore] => (because x1=-x1.T) =>
# x2[:,:ncore] += -H' * x1[:ncore] => (because x2-x2.T) =>
# x2[:ncore] += H' * x1[:ncore]
va, vc = casscf.update_jk_in_ah(mo, x1, casdm1, eris)
x2[ncore:nocc] += va
x2[:ncore,ncore:] += vc
# (pr<->qs)
x2 = x2 - x2.T
return casscf.pack_uniq_var(x2)
return g_orb, gorb_update, h_op, h_diag
[docs]
def rotate_orb_cc(casscf, mo, fcivec, fcasdm1, fcasdm2, eris, x0_guess=None,
conv_tol_grad=1e-4, max_stepsize=None, verbose=None):
log = logger.new_logger(casscf, verbose)
if max_stepsize is None:
max_stepsize = casscf.max_stepsize
t3m = (logger.process_clock(), logger.perf_counter())
u = 1
g_orb, gorb_update, h_op, h_diag = \
casscf.gen_g_hop(mo, u, fcasdm1(), fcasdm2(), eris)
g_kf = g_orb
norm_gkf = norm_gorb = numpy.linalg.norm(g_orb)
log.debug(' |g|=%5.3g', norm_gorb)
t3m = log.timer('gen h_op', *t3m)
if norm_gorb < conv_tol_grad*.3:
u = casscf.update_rotate_matrix(g_orb*0)
yield u, g_orb, 1, x0_guess
return
def precond(x, e):
hdiagd = h_diag-(e-casscf.ah_level_shift)
hdiagd[abs(hdiagd)<1e-8] = 1e-8
x = x/hdiagd
norm_x = numpy.linalg.norm(x)
x *= 1/norm_x
#if norm_x < 1e-2:
# x *= 1e-2/norm_x
return x
jkcount = 0
if x0_guess is None:
x0_guess = g_orb
imic = 0
dr = 0
ikf = 0
g_op = lambda: g_orb
problem_size = g_orb.size
for ah_end, ihop, w, dxi, hdxi, residual, seig \
in ciah.davidson_cc(h_op, g_op, precond, x0_guess,
tol=casscf.ah_conv_tol, max_cycle=casscf.ah_max_cycle,
lindep=casscf.ah_lindep, verbose=log):
# residual = v[0] * (g+(h-e)x) ~ v[0] * grad
norm_residual = numpy.linalg.norm(residual)
if (ah_end or ihop == casscf.ah_max_cycle or # make sure to use the last step
((norm_residual < casscf.ah_start_tol) and (ihop >= casscf.ah_start_cycle)) or
(seig < casscf.ah_lindep)):
imic += 1
dxmax = numpy.max(abs(dxi))
if ihop == problem_size:
log.debug1('... Hx=g fully converged for small systems')
#max_stepsize = casscf.max_stepsize * 10
elif dxmax > max_stepsize:
scale = max_stepsize / dxmax
log.debug1('... scale rotation size %g', scale)
dxi *= scale
hdxi *= scale
g_orb = g_orb + hdxi
dr = dr + dxi
norm_gorb = numpy.linalg.norm(g_orb)
norm_dxi = numpy.linalg.norm(dxi)
norm_dr = numpy.linalg.norm(dr)
log.debug(' imic %2d(%2d) |g[o]|=%5.3g |dxi|=%5.3g '
'max(|x|)=%5.3g |dr|=%5.3g eig=%5.3g seig=%5.3g',
imic, ihop, norm_gorb, norm_dxi,
dxmax, norm_dr, w, seig)
ikf += 1
if ikf > 1 and norm_gorb > norm_gkf*casscf.ah_grad_trust_region:
g_orb = g_orb - hdxi
dr -= dxi
#norm_gorb = numpy.linalg.norm(g_orb)
log.debug('|g| >> keyframe, Restore previouse step')
break
elif (norm_gorb < conv_tol_grad*.3):
break
elif (ikf >= max(casscf.kf_interval, -numpy.log(norm_dr+1e-7)) or
# Insert keyframe if the keyframe and the estimated grad
# are very different
norm_gorb < norm_gkf/casscf.kf_trust_region):
ikf = 0
u = casscf.update_rotate_matrix(dr, u)
t3m = log.timer('aug_hess in %2d inner iters' % imic, *t3m)
yield u, g_kf, ihop+jkcount, dxi
t3m = (logger.process_clock(), logger.perf_counter())
# TODO: test whether to update h_op, h_diag to change the orbital hessian.
# It leads to the different hessian operations in the same davidson
# diagonalization procedure. This is generally a bad approximation because it
# results in ill-defined hessian eigenvalue in the davidson algorithm. But in
# certain cases, it is a small perturbation that help the mcscf optimization
# algorithm move out of local minimum
# h_op, h_diag = \
# casscf.gen_g_hop(mo, u, fcasdm1(), fcasdm2(), eris)[2:4]
g_kf1 = gorb_update(u, fcivec())
jkcount += 1
norm_gkf1 = numpy.linalg.norm(g_kf1)
norm_dg = numpy.linalg.norm(g_kf1-g_orb)
log.debug(' |g|=%5.3g (keyframe), |g-correction|=%5.3g',
norm_gkf1, norm_dg)
#
# Special treatment if out of trust region
#
if (norm_dg > norm_gorb*casscf.ah_grad_trust_region and
norm_gkf1 > norm_gkf and
norm_gkf1 > norm_gkf*casscf.ah_grad_trust_region):
log.debug(' Keyframe |g|=%5.3g |g_last| =%5.3g out of trust region',
norm_gkf1, norm_gorb)
# Slightly moving forward, not completely restoring last step.
# In some cases, the optimization moves out of trust region in the first micro
# iteration. The small forward step can ensure the orbital changes in the
# current iteration.
dr = -dxi * (1 - casscf.scale_restoration)
g_kf = g_kf1
break
t3m = log.timer('gen h_op', *t3m)
g_orb = g_kf = g_kf1
norm_gorb = norm_gkf = norm_gkf1
dr[:] = 0
u = casscf.update_rotate_matrix(dr, u)
yield u, g_kf, ihop+jkcount, dxi
[docs]
def kernel(casscf, mo_coeff, tol=1e-7, conv_tol_grad=None,
ci0=None, callback=None, verbose=logger.NOTE, dump_chk=True):
'''quasi-newton CASSCF optimization driver
'''
from pyscf.mcscf.addons import StateAverageMCSCFSolver
log = logger.new_logger(casscf, verbose)
cput0 = (logger.process_clock(), logger.perf_counter())
log.debug('Start 1-step CASSCF')
if callback is None:
callback = casscf.callback
if ci0 is None:
ci0 = casscf.ci
mo = mo_coeff
nmo = mo_coeff.shape[1]
ncore = casscf.ncore
ncas = casscf.ncas
nocc = ncore + ncas
eris = casscf.ao2mo(mo)
e_tot, e_cas, fcivec = casscf.casci(mo, ci0, eris, log, locals())
# macro iterations are needed when added solvent model
# if ncas == nmo and not casscf.internal_rotation:
# if casscf.canonicalization:
# log.debug('CASSCF canonicalization')
# mo, fcivec, mo_energy = casscf.canonicalize(mo, fcivec, eris,
# casscf.sorting_mo_energy,
# casscf.natorb, verbose=log)
# else:
# mo_energy = None
# return True, e_tot, e_cas, fcivec, mo, mo_energy
if conv_tol_grad is None:
conv_tol_grad = numpy.sqrt(tol)
logger.info(casscf, 'Set conv_tol_grad to %g', conv_tol_grad)
conv_tol_ddm = conv_tol_grad * 3
conv = False
totmicro = totinner = 0
norm_gorb = norm_gci = -1
de, elast = e_tot, e_tot
r0 = None
t1m = log.timer('Initializing 1-step CASSCF', *cput0)
casdm1, casdm2 = casscf.fcisolver.make_rdm12(fcivec, ncas, casscf.nelecas)
norm_ddm = 1e2
casdm1_prev = casdm1_last = casdm1
t3m = t2m = log.timer('CAS DM', *t1m)
imacro = 0
dr0 = None
while not conv and imacro < casscf.max_cycle_macro:
imacro += 1
max_cycle_micro = casscf.micro_cycle_scheduler(locals())
max_stepsize = casscf.max_stepsize_scheduler(locals())
imicro = 0
rota = casscf.rotate_orb_cc(mo, lambda:fcivec, lambda:casdm1, lambda:casdm2,
eris, r0, conv_tol_grad*.3, max_stepsize, log)
for u, g_orb, njk, r0 in rota:
imicro += 1
norm_gorb = numpy.linalg.norm(g_orb)
if imicro == 1:
norm_gorb0 = norm_gorb
norm_t = numpy.linalg.norm(u-numpy.eye(nmo))
t3m = log.timer('orbital rotation', *t3m)
if imicro >= max_cycle_micro:
log.debug('micro %2d |u-1|=%5.3g |g[o]|=%5.3g',
imicro, norm_t, norm_gorb)
break
casdm1, casdm2, gci, fcivec = \
casscf.update_casdm(mo, u, fcivec, e_cas, eris, locals())
norm_ddm = numpy.linalg.norm(casdm1 - casdm1_last)
norm_ddm_micro = numpy.linalg.norm(casdm1 - casdm1_prev)
casdm1_prev = casdm1
t3m = log.timer('update CAS DM', *t3m)
if isinstance(gci, numpy.ndarray):
norm_gci = numpy.linalg.norm(gci)
log.debug('micro %2d |u-1|=%5.3g |g[o]|=%5.3g |g[c]|=%5.3g |ddm|=%5.3g',
imicro, norm_t, norm_gorb, norm_gci, norm_ddm)
else:
norm_gci = None
log.debug('micro %2d |u-1|=%5.3g |g[o]|=%5.3g |g[c]|=%s |ddm|=%5.3g',
imicro, norm_t, norm_gorb, norm_gci, norm_ddm)
if callable(callback):
callback(locals())
t3m = log.timer('micro iter %2d'%imicro, *t3m)
if (norm_t < conv_tol_grad or
(norm_gorb < conv_tol_grad*.5 and
(norm_ddm < conv_tol_ddm*.4 or norm_ddm_micro < conv_tol_ddm*.4))):
break
rota.close()
rota = None
totmicro += imicro
totinner += njk
eris = None
# keep u, g_orb in locals() so that they can be accessed by callback
mo = casscf.rotate_mo(mo, u, log)
eris = casscf.ao2mo(mo)
t2m = log.timer('update eri', *t3m)
max_offdiag_u = numpy.abs(numpy.triu(u, 1)).max()
if max_offdiag_u < casscf.small_rot_tol:
small_rot = True
else:
small_rot = False
if not isinstance(casscf, StateAverageMCSCFSolver):
# The fcivec from builtin FCI solver is a numpy.ndarray
if not isinstance(fcivec, numpy.ndarray):
fcivec = small_rot
else:
newvecs = []
for subvec in fcivec:
# CI vector obtained by builtin FCI is a numpy array
if not isinstance(subvec, numpy.ndarray):
newvecs.append(small_rot)
else:
newvecs.append(subvec)
fcivec = newvecs
e_tot, e_cas, fcivec = casscf.casci(mo, fcivec, eris, log, locals())
casdm1, casdm2 = casscf.fcisolver.make_rdm12(fcivec, ncas, casscf.nelecas)
norm_ddm = numpy.linalg.norm(casdm1 - casdm1_last)
casdm1_prev = casdm1_last = casdm1
log.timer('CASCI solver', *t2m)
t3m = t2m = t1m = log.timer('macro iter %2d'%imacro, *t1m)
de, elast = e_tot - elast, e_tot
if (abs(de) < tol and norm_gorb0 < conv_tol_grad and
norm_ddm < conv_tol_ddm and
(max_offdiag_u < casscf.small_rot_tol or casscf.small_rot_tol == 0)):
conv = True
if dump_chk and casscf.chkfile:
casscf.dump_chk(locals())
if callable(callback):
callback(locals())
if conv:
log.info('1-step CASSCF converged in %3d macro (%3d JK %3d micro) steps',
imacro, totinner, totmicro)
else:
log.info('1-step CASSCF not converged, %3d macro (%3d JK %3d micro) steps',
imacro, totinner, totmicro)
if casscf.canonicalization:
log.info('CASSCF canonicalization')
mo, fcivec, mo_energy = \
casscf.canonicalize(mo, fcivec, eris, casscf.sorting_mo_energy,
casscf.natorb, casdm1, log)
if casscf.natorb and dump_chk: # dump_chk may save casdm1
occ, ucas = casscf._eig(-casdm1, ncore, nocc)
casdm1 = numpy.diag(-occ)
else:
if casscf.natorb:
# FIXME (pyscf-2.0): Whether to transform natural orbitals in
# active space when this flag is enabled?
log.warn('The attribute natorb of mcscf object affects only the '
'orbital canonicalization.\n'
'If you would like to get natural orbitals in active space '
'without touching core and external orbitals, an explicit '
'call to mc.cas_natorb_() is required')
mo_energy = None
if dump_chk and casscf.chkfile:
casscf.dump_chk(locals())
log.timer('1-step CASSCF', *cput0)
return conv, e_tot, e_cas, fcivec, mo, mo_energy
[docs]
def as_scanner(mc):
'''Generating a scanner for CASSCF PES.
The returned solver is a function. This function requires one argument
"mol" as input and returns total CASSCF energy.
The solver will automatically use the results of last calculation as the
initial guess of the new calculation. All parameters of MCSCF object
(conv_tol, max_memory etc) are automatically applied in the solver.
Note scanner has side effects. It may change many underlying objects
(_scf, with_df, with_x2c, ...) during calculation.
Examples:
>>> from pyscf import gto, scf, mcscf
>>> mol = gto.M(atom='N 0 0 0; N 0 0 1.2', verbose=0)
>>> mc_scanner = mcscf.CASSCF(scf.RHF(mol), 4, 4).as_scanner()
>>> e = mc_scanner(gto.M(atom='N 0 0 0; N 0 0 1.1'))
>>> e = mc_scanner(gto.M(atom='N 0 0 0; N 0 0 1.5'))
'''
if isinstance(mc, lib.SinglePointScanner):
return mc
logger.info(mc, 'Create scanner for %s', mc.__class__)
name = mc.__class__.__name__ + CASSCF_Scanner.__name_mixin__
return lib.set_class(CASSCF_Scanner(mc), (CASSCF_Scanner, mc.__class__), name)
[docs]
class CASSCF_Scanner(lib.SinglePointScanner):
def __init__(self, mc):
self.__dict__.update(mc.__dict__)
self._scf = mc._scf.as_scanner()
def __call__(self, mol_or_geom, **kwargs):
from pyscf.mcscf.addons import project_init_guess
if isinstance(mol_or_geom, gto.MoleBase):
mol = mol_or_geom
else:
mol = self.mol.set_geom_(mol_or_geom, inplace=False)
# These properties can be updated when calling mf_scanner(mol) if
# they are shared with mc._scf. In certain scenario the properties
# may be created for mc separately, e.g. when mcscf.approx_hessian is
# called. For safety, the code below explicitly resets these
# properties.
self.reset (mol)
for key in ('with_df', 'with_x2c', 'with_solvent', 'with_dftd3'):
sub_mod = getattr(self, key, None)
if sub_mod:
sub_mod.reset(mol)
mf_scanner = self._scf
mf_scanner(mol)
self.mol = mol
if self.mo_coeff is None:
mo = mf_scanner.mo_coeff
else:
mo = self.mo_coeff
mo = project_init_guess(self, mo)
e_tot = self.kernel(mo, self.ci)[0]
return e_tot
[docs]
def max_stepsize_scheduler(casscf, envs):
if not WITH_STEPSIZE_SCHEDULER:
return casscf.max_stepsize
_max_stepsize = envs.get ('max_stepsize', None)
if _max_stepsize is None:
_max_stepsize = casscf.max_stepsize
if envs['de'] > -casscf.conv_tol: # Avoid total energy increasing
_max_stepsize *= .3
logger.debug(casscf, 'set max_stepsize to %g', _max_stepsize)
else:
_max_stepsize = (casscf.max_stepsize*_max_stepsize)**.5
casscf._max_stepsize = _max_stepsize # for inspection by user
return _max_stepsize
# To extend CASSCF for certain CAS space solver, it can be done by assign an
# object or a module to CASSCF.fcisolver. The fcisolver object or module
# should at least have three member functions "kernel" (wfn for given
# hamiltonian), "make_rdm12" (1- and 2-pdm), "absorb_h1e" (effective
# 2e-hamiltonian) in 1-step CASSCF solver, and two member functions "kernel"
# and "make_rdm12" in 2-step CASSCF solver
[docs]
class CASSCF(casci.CASBase):
__doc__ = casci.CASBase.__doc__ + '''
Extra attributes for CASSCF:
conv_tol : float
Converge threshold. Default is 1e-7
conv_tol_grad : float
Converge threshold for CI gradients and orbital rotation gradients.
If not specified, it is set to sqrt(conv_tol).
max_stepsize : float
The step size for orbital rotation. Small step (0.005 - 0.05) is prefered.
Default is 0.02.
max_cycle_macro : int
Max number of macro iterations. Default is 50.
max_cycle_micro : int
Max number of micro iterations in each macro iteration. Depending on
systems, increasing this value might reduce the total macro
iterations. Generally, 2 - 5 steps should be enough. Default is 4.
small_rot_tol : float
Threshold for orbital rotation to be considered small. If the largest orbital
rotation is smaller than this value, the CI solver will restart from the
previous iteration if supported.
Default is 0.01
ah_level_shift : float, for AH solver.
Level shift for the Davidson diagonalization in AH solver. Default is 1e-8.
ah_conv_tol : float, for AH solver.
converge threshold for AH solver. Default is 1e-12.
ah_max_cycle : float, for AH solver.
Max number of iterations allowd in AH solver. Default is 30.
ah_lindep : float, for AH solver.
Linear dependence threshold for AH solver. Default is 1e-14.
ah_start_tol : flat, for AH solver.
In AH solver, the orbital rotation is started without completely solving the AH problem.
This value is to control the start point. Default is 2.5.
ah_start_cycle : int, for AH solver.
In AH solver, the orbital rotation is started without completely solving the AH problem.
This value is to control the start point. Default is 3.
``ah_conv_tol``, ``ah_max_cycle``, ``ah_lindep``, ``ah_start_tol`` and ``ah_start_cycle``
can affect the accuracy and performance of CASSCF solver. Lower
``ah_conv_tol`` and ``ah_lindep`` might improve the accuracy of CASSCF
optimization, but decrease the performance.
>>> from pyscf import gto, scf, mcscf
>>> mol = gto.M(atom='N 0 0 0; N 0 0 1', basis='ccpvdz', verbose=0)
>>> mf = scf.UHF(mol)
>>> mf.scf()
>>> mc = mcscf.CASSCF(mf, 6, 6)
>>> mc.conv_tol = 1e-10
>>> mc.ah_conv_tol = 1e-5
>>> mc.kernel()[0]
-109.044401898486001
>>> mc.ah_conv_tol = 1e-10
>>> mc.kernel()[0]
-109.044401887945668
chkfile : str
Checkpoint file to save the intermediate orbitals during the CASSCF optimization.
Default is the checkpoint file of mean field object.
ci_response_space : int
subspace size to solve the CI vector response. Default is 3.
callback : function(envs_dict) => None
callback function takes one dict as the argument which is
generated by the builtin function :func:`locals`, so that the
callback function can access all local variables in the current
environment.
scale_restoration : float
When a step of orbital rotation moves out of trust region, the
orbital optimization will be restored to previous state and the
step size of the orbital rotation needs to be reduced.
scale_restoration controls how much to scale down the step size.
Saved results
e_tot : float
Total MCSCF energy (electronic energy plus nuclear repulsion)
e_cas : float
CAS space FCI energy
ci : ndarray
CAS space FCI coefficients
mo_coeff : ndarray
Optimized CASSCF orbitals coefficients. When canonicalization is
specified, the returned orbitals make the general Fock matrix
(Fock operator on top of MCSCF 1-particle density matrix)
diagonalized within each subspace (core, active, external). If
natorb (natural orbitals in active space) is enabled, the active
segment of mo_coeff is transformed to natural orbitals.
mo_energy : ndarray
Diagonal elements of general Fock matrix (in mo_coeff
representation).
Examples:
>>> from pyscf import gto, scf, mcscf
>>> mol = gto.M(atom='N 0 0 0; N 0 0 1', basis='ccpvdz', verbose=0)
>>> mf = scf.RHF(mol)
>>> mf.scf()
>>> mc = mcscf.CASSCF(mf, 6, 6)
>>> mc.kernel()[0]
-109.044401882238134
'''
# the max orbital rotation and CI increment, prefer small step size
max_stepsize = getattr(__config__, 'mcscf_mc1step_CASSCF_max_stepsize', .02)
max_cycle_macro = getattr(__config__, 'mcscf_mc1step_CASSCF_max_cycle_macro', 50)
max_cycle_micro = getattr(__config__, 'mcscf_mc1step_CASSCF_max_cycle_micro', 4)
conv_tol = getattr(__config__, 'mcscf_mc1step_CASSCF_conv_tol', 1e-7)
conv_tol_grad = getattr(__config__, 'mcscf_mc1step_CASSCF_conv_tol_grad', None)
# for augmented hessian
ah_level_shift = getattr(__config__, 'mcscf_mc1step_CASSCF_ah_level_shift', 1e-8)
ah_conv_tol = getattr(__config__, 'mcscf_mc1step_CASSCF_ah_conv_tol', 1e-12)
ah_max_cycle = getattr(__config__, 'mcscf_mc1step_CASSCF_ah_max_cycle', 30)
ah_lindep = getattr(__config__, 'mcscf_mc1step_CASSCF_ah_lindep', 1e-14)
# * ah_start_tol and ah_start_cycle control the start point to use AH step.
# In function rotate_orb_cc, the orbital rotation is carried out with the
# approximate aug_hessian step after a few davidson updates of the AH eigen
# problem. Reducing ah_start_tol or increasing ah_start_cycle will delay
# the start point of orbital rotation.
# * We can do early ah_start since it only affect the first few iterations.
# The start tol will be reduced when approach the convergence point.
# * Be careful with the SYMMETRY BROKEN caused by ah_start_tol/ah_start_cycle.
# ah_start_tol/ah_start_cycle actually approximates the hessian to reduce
# the J/K evaluation required by AH. When the system symmetry is higher
# than the one given by mol.symmetry/mol.groupname, symmetry broken might
# occur due to this approximation, e.g. with the default ah_start_tol,
# C2 (16o, 8e) under D2h symmetry might break the degeneracy between
# pi_x, pi_y orbitals since pi_x, pi_y belong to different irreps. It can
# be fixed by increasing the accuracy of AH solver, e.g.
# ah_start_tol = 1e-8; ah_conv_tol = 1e-10
# * Classic AH can be simulated by setting eg
# ah_start_tol = 1e-7
# max_stepsize = 1.5
# ah_grad_trust_region = 1e6
# ah_grad_trust_region allow gradients being increased in AH optimization
ah_start_tol = getattr(__config__, 'mcscf_mc1step_CASSCF_ah_start_tol', 2.5)
ah_start_cycle = getattr(__config__, 'mcscf_mc1step_CASSCF_ah_start_cycle', 3)
ah_grad_trust_region = getattr(__config__, 'mcscf_mc1step_CASSCF_ah_grad_trust_region', 3.0)
internal_rotation = getattr(__config__, 'mcscf_mc1step_CASSCF_internal_rotation', False)
ci_response_space = getattr(__config__, 'mcscf_mc1step_CASSCF_ci_response_space', 4)
ci_grad_trust_region = getattr(__config__, 'mcscf_mc1step_CASSCF_ci_grad_trust_region', 3.0)
with_dep4 = getattr(__config__, 'mcscf_mc1step_CASSCF_with_dep4', False)
chk_ci = getattr(__config__, 'mcscf_mc1step_CASSCF_chk_ci', False)
kf_interval = getattr(__config__, 'mcscf_mc1step_CASSCF_kf_interval', 4)
kf_trust_region = getattr(__config__, 'mcscf_mc1step_CASSCF_kf_trust_region', 3.0)
ao2mo_level = getattr(__config__, 'mcscf_mc1step_CASSCF_ao2mo_level', 2)
natorb = getattr(__config__, 'mcscf_mc1step_CASSCF_natorb', False)
canonicalization = getattr(__config__, 'mcscf_mc1step_CASSCF_canonicalization', True)
sorting_mo_energy = getattr(__config__, 'mcscf_mc1step_CASSCF_sorting_mo_energy', False)
scale_restoration = getattr(__config__, 'mcscf_mc1step_CASSCF_scale_restoration', 0.5)
small_rot_tol = getattr(__config__, 'mcscf_mc1step_CASSCF_small_rot_tol', 0.01)
extrasym = None
callback = None
_keys = {
'max_stepsize', 'max_cycle_macro', 'max_cycle_micro', 'conv_tol',
'conv_tol_grad', 'ah_level_shift', 'ah_conv_tol', 'ah_max_cycle',
'ah_lindep', 'ah_start_tol', 'ah_start_cycle', 'ah_grad_trust_region',
'internal_rotation', 'ci_response_space', 'ci_grad_trust_region',
'with_dep4', 'chk_ci', 'kf_interval', 'kf_trust_region',
'fcisolver_max_cycle', 'fcisolver_conv_tol', 'natorb',
'canonicalization', 'sorting_mo_energy', 'scale_restoration',
'small_rot_tol', 'extrasym', 'callback',
'frozen', 'chkfile', 'fcisolver', 'e_tot', 'e_cas', 'ci', 'mo_coeff',
'mo_energy', 'converged',
}
def __init__(self, mf_or_mol, ncas=0, nelecas=0, ncore=None, frozen=None):
casci.CASBase.__init__(self, mf_or_mol, ncas, nelecas, ncore)
self.frozen = frozen
self.chkfile = self._scf.chkfile
self.fcisolver.max_cycle = getattr(__config__,
'mcscf_mc1step_CASSCF_fcisolver_max_cycle', 50)
self.fcisolver.conv_tol = getattr(__config__,
'mcscf_mc1step_CASSCF_fcisolver_conv_tol', 1e-8)
##################################################
# don't modify the following attributes, they are not input options
self.e_tot = None
self.e_cas = None
self.ci = None
self.mo_coeff = self._scf.mo_coeff
self.mo_energy = self._scf.mo_energy
self.converged = False
self._max_stepsize = None
__getstate__, __setstate__ = lib.generate_pickle_methods(
excludes=('chkfile', 'callback'))
[docs]
def dump_flags(self, verbose=None):
log = logger.new_logger(self, verbose)
log.info('')
log.info('******** %s ********', self.__class__)
ncore = self.ncore
ncas = self.ncas
nvir = self.mo_coeff.shape[1] - ncore - ncas
log.info('CAS (%de+%de, %do), ncore = %d, nvir = %d',
self.nelecas[0], self.nelecas[1], ncas, ncore, nvir)
if self.frozen is not None:
log.info('frozen orbitals %s', str(self.frozen))
if self.extrasym is not None:
log.info('Extra symmetry labels:\n%s', str(self.extrasym))
log.info('max_cycle_macro = %d', self.max_cycle_macro)
log.info('max_cycle_micro = %d', self.max_cycle_micro)
log.info('conv_tol = %g', self.conv_tol)
log.info('conv_tol_grad = %s', self.conv_tol_grad)
log.info('orbital rotation max_stepsize = %g', self.max_stepsize)
log.info('orbital rotation threshold for CI restart = %g', self.small_rot_tol)
log.info('augmented hessian ah_max_cycle = %d', self.ah_max_cycle)
log.info('augmented hessian ah_conv_tol = %g', self.ah_conv_tol)
log.info('augmented hessian ah_linear dependence = %g', self.ah_lindep)
log.info('augmented hessian ah_level shift = %g', self.ah_level_shift)
log.info('augmented hessian ah_start_tol = %g', self.ah_start_tol)
log.info('augmented hessian ah_start_cycle = %d', self.ah_start_cycle)
log.info('augmented hessian ah_grad_trust_region = %g', self.ah_grad_trust_region)
log.info('kf_trust_region = %g', self.kf_trust_region)
log.info('kf_interval = %d', self.kf_interval)
log.info('ci_response_space = %d', self.ci_response_space)
log.info('ci_grad_trust_region = %d', self.ci_grad_trust_region)
log.info('with_dep4 %d', self.with_dep4)
log.info('natorb = %s', self.natorb)
log.info('canonicalization = %s', self.canonicalization)
log.info('sorting_mo_energy = %s', self.sorting_mo_energy)
log.info('ao2mo_level = %d', self.ao2mo_level)
log.info('chkfile = %s', self.chkfile)
log.info('max_memory %d MB (current use %d MB)',
self.max_memory, lib.current_memory()[0])
log.info('internal_rotation = %s', self.internal_rotation)
if getattr(self.fcisolver, 'dump_flags', None):
self.fcisolver.dump_flags(self.verbose)
if self.mo_coeff is None:
log.error('Orbitals for CASSCF are not specified. The relevant SCF '
'object may not be initialized.')
if (getattr(self._scf, 'with_solvent', None) and
not getattr(self, 'with_solvent', None)):
log.warn('''Solvent model %s was found at SCF level but not applied to the CASSCF object.
The SCF solvent model will not be applied to the current CASSCF calculation.
To enable the solvent model for CASSCF, the following code needs to be called
from pyscf import solvent
mc = mcscf.CASSCF(...)
mc = solvent.ddCOSMO(mc)
''',
self._scf.with_solvent.__class__)
return self
[docs]
def kernel(self, mo_coeff=None, ci0=None, callback=None, _kern=kernel):
'''
Returns:
Five elements, they are
total energy,
active space CI energy,
the active space FCI wavefunction coefficients or DMRG wavefunction ID,
the MCSCF canonical orbital coefficients,
the MCSCF canonical orbital coefficients.
They are attributes of mcscf object, which can be accessed by
.e_tot, .e_cas, .ci, .mo_coeff, .mo_energy
'''
if mo_coeff is None:
mo_coeff = self.mo_coeff
else: # overwrite self.mo_coeff because it is needed in many methods of this class
self.mo_coeff = mo_coeff
if callback is None: callback = self.callback
if ci0 is None:
ci0 = self.ci
self.check_sanity()
self.dump_flags()
self.converged, self.e_tot, self.e_cas, self.ci, \
self.mo_coeff, self.mo_energy = \
_kern(self, mo_coeff,
tol=self.conv_tol, conv_tol_grad=self.conv_tol_grad,
ci0=ci0, callback=callback, verbose=self.verbose)
logger.note(self, 'CASSCF energy = %#.15g', self.e_tot)
self._finalize()
return self.e_tot, self.e_cas, self.ci, self.mo_coeff, self.mo_energy
[docs]
def mc1step(self, mo_coeff=None, ci0=None, callback=None):
return self.kernel(mo_coeff, ci0, callback)
[docs]
def mc2step(self, mo_coeff=None, ci0=None, callback=None):
from pyscf.mcscf import mc2step
return self.kernel(mo_coeff, ci0, callback, mc2step.kernel)
[docs]
def casci(self, mo_coeff, ci0=None, eris=None, verbose=None, envs=None):
log = logger.new_logger(self, verbose)
fcasci = _fake_h_for_fast_casci(self, mo_coeff, eris)
e_tot, e_cas, fcivec = casci.kernel(fcasci, mo_coeff, ci0, log,
envs=envs)
if not isinstance(e_cas, (float, numpy.number)):
raise RuntimeError('Multiple roots are detected in fcisolver. '
'CASSCF does not know which state to optimize.\n'
'See also mcscf.state_average or mcscf.state_specific for excited states.')
elif numpy.ndim(e_cas) != 0:
# This is a workaround for external CI solver compatibility.
e_cas = e_cas[0]
if envs is not None and log.verbose >= logger.INFO:
log.debug('CAS space CI energy = %#.15g', e_cas)
if getattr(self.fcisolver, 'spin_square', None):
try:
ss = self.fcisolver.spin_square(fcivec, self.ncas, self.nelecas)
except NotImplementedError:
ss = None
else:
ss = None
if 'imicro' in envs: # Within CASSCF iteration
if ss is None:
log.info('macro iter %3d (%3d JK %3d micro), '
'CASSCF E = %#.15g dE = % .8e',
envs['imacro'], envs['njk'], envs['imicro'],
e_tot, e_tot-envs['elast'])
else:
log.info('macro iter %3d (%3d JK %3d micro), '
'CASSCF E = %#.15g dE = % .8e S^2 = %.7f',
envs['imacro'], envs['njk'], envs['imicro'],
e_tot, e_tot-envs['elast'], ss[0])
if 'norm_gci' in envs and envs['norm_gci'] is not None:
log.info(' |grad[o]|=%5.3g '
'|grad[c]|=%5.3g |ddm|=%5.3g |maxRot[o]|=%5.3g',
envs['norm_gorb0'],
envs['norm_gci'], envs['norm_ddm'], envs['max_offdiag_u'])
else:
log.info(' |grad[o]|=%5.3g |ddm|=%5.3g |maxRot[o]|=%5.3g',
envs['norm_gorb0'], envs['norm_ddm'], envs['max_offdiag_u'])
else: # Initialization step
if ss is None:
log.info('CASCI E = %#.15g', e_tot)
else:
log.info('CASCI E = %#.15g S^2 = %.7f', e_tot, ss[0])
return e_tot, e_cas, fcivec
as_scanner = as_scanner
[docs]
def uniq_var_indices(self, nmo, ncore, ncas, frozen):
nocc = ncore + ncas
mask = numpy.zeros((nmo,nmo),dtype=bool)
mask[ncore:nocc,:ncore] = True
mask[nocc:,:nocc] = True
if self.internal_rotation:
mask[ncore:nocc,ncore:nocc][numpy.tril_indices(ncas,-1)] = True
if self.extrasym is not None:
extrasym = numpy.asarray(self.extrasym)
# Allow rotation only if extra symmetry labels are the same
extrasym_allowed = extrasym.reshape(-1, 1) == extrasym
mask = mask * extrasym_allowed
if frozen is not None:
if isinstance(frozen, (int, numpy.integer)):
mask[:frozen] = mask[:,:frozen] = False
else:
frozen = numpy.asarray(frozen)
mask[frozen] = mask[:,frozen] = False
return mask
[docs]
def pack_uniq_var(self, mat):
nmo = self.mo_coeff.shape[1]
idx = self.uniq_var_indices(nmo, self.ncore, self.ncas, self.frozen)
return mat[idx]
# to anti symmetric matrix
[docs]
def unpack_uniq_var(self, v):
nmo = self.mo_coeff.shape[1]
idx = self.uniq_var_indices(nmo, self.ncore, self.ncas, self.frozen)
mat = numpy.zeros((nmo,nmo))
mat[idx] = v
return mat - mat.T
[docs]
def update_rotate_matrix(self, dx, u0=1):
dr = self.unpack_uniq_var(dx)
return numpy.dot(u0, expmat(dr))
gen_g_hop = gen_g_hop
rotate_orb_cc = rotate_orb_cc
[docs]
def update_ao2mo(self, mo):
raise DeprecationWarning('update_ao2mo was obsoleted since pyscf v1.0. '
'Use .ao2mo method instead')
[docs]
def ao2mo(self, mo_coeff=None):
if mo_coeff is None: mo_coeff = self.mo_coeff
# nmo = mo.shape[1]
# ncore = self.ncore
# ncas = self.ncas
# nocc = ncore + ncas
# eri = pyscf.ao2mo.incore.full(self._scf._eri, mo)
# eri = pyscf.ao2mo.restore(1, eri, nmo)
# eris = lambda:None
# eris.j_cp = numpy.einsum('iipp->ip', eri[:ncore,:ncore,:,:])
# eris.k_cp = numpy.einsum('ippi->ip', eri[:ncore,:,:,:ncore])
# eris.vhf_c =(numpy.einsum('iipq->pq', eri[:ncore,:ncore,:,:])*2
# -numpy.einsum('ipqi->pq', eri[:ncore,:,:,:ncore]))
# eris.ppaa = numpy.asarray(eri[:,:,ncore:nocc,ncore:nocc], order='C')
# eris.papa = numpy.asarray(eri[:,ncore:nocc,:,ncore:nocc], order='C')
# return eris
return mc_ao2mo._ERIS(self, mo_coeff, method='incore',
level=self.ao2mo_level)
get_h2eff = CASCI.get_h2eff
[docs]
def update_jk_in_ah(self, mo, r, casdm1, eris):
# J3 = eri_popc * pc + eri_cppo * cp
# K3 = eri_ppco * pc + eri_pcpo * cp
# J4 = eri_pcpa * pa + eri_appc * ap
# K4 = eri_ppac * pa + eri_papc * ap
ncore = self.ncore
ncas = self.ncas
nocc = ncore + ncas
dm3 = reduce(numpy.dot, (mo[:,:ncore], r[:ncore,ncore:], mo[:,ncore:].T))
dm3 = dm3 + dm3.T
dm4 = reduce(numpy.dot, (mo[:,ncore:nocc], casdm1, r[ncore:nocc], mo.T))
dm4 = dm4 + dm4.T
vj, vk = self.get_jk(self.mol, (dm3,dm3*2+dm4))
va = reduce(numpy.dot, (casdm1, mo[:,ncore:nocc].T, vj[0]*2-vk[0], mo))
vc = reduce(numpy.dot, (mo[:,:ncore].T, vj[1]*2-vk[1], mo[:,ncore:]))
return va, vc
# hessian_co exactly expands up to first order of H
# update_casdm exand to approx 2nd order of H
[docs]
def update_casdm(self, mo, u, fcivec, e_cas, eris, envs={}):
nmo = mo.shape[1]
rmat = u - numpy.eye(nmo)
#g = hessian_co(self, mo, rmat, fcivec, e_cas, eris)
### hessian_co part start ###
ncas = self.ncas
nelecas = self.nelecas
ncore = self.ncore
nocc = ncore + ncas
uc = u[:,:ncore]
ua = u[:,ncore:nocc].copy()
ra = rmat[:,ncore:nocc].copy()
h1e_mo = reduce(numpy.dot, (mo.T, self.get_hcore(), mo))
ddm = numpy.dot(uc, uc.T) * 2
ddm[numpy.diag_indices(ncore)] -= 2
if self.with_dep4:
mo1 = numpy.dot(mo, u)
mo1_cas = mo1[:,ncore:nocc]
dm_core = numpy.dot(mo1[:,:ncore], mo1[:,:ncore].T) * 2
vj, vk = self._scf.get_jk(self.mol, dm_core)
h1 =(reduce(numpy.dot, (ua.T, h1e_mo, ua)) +
reduce(numpy.dot, (mo1_cas.T, vj-vk*.5, mo1_cas)))
eris._paaa = self._exact_paaa(mo, u)
h2 = eris._paaa[ncore:nocc]
vj = vk = None
else:
p1aa = numpy.empty((nmo,ncas,ncas**2))
paa1 = numpy.empty((nmo,ncas**2,ncas))
jk = reduce(numpy.dot, (ua.T, eris.vhf_c, ua))
for i in range(nmo):
jbuf = eris.ppaa[i]
kbuf = eris.papa[i]
jk += (numpy.einsum('quv,q->uv', jbuf, ddm[i]) -
numpy.einsum('uqv,q->uv', kbuf, ddm[i]) * .5)
p1aa[i] = lib.dot(ua.T, jbuf.reshape(nmo,-1))
paa1[i] = lib.dot(kbuf.transpose(0,2,1).reshape(-1,nmo), ra)
h1 = reduce(numpy.dot, (ua.T, h1e_mo, ua)) + jk
aa11 = lib.dot(ua.T, p1aa.reshape(nmo,-1)).reshape((ncas,)*4)
aaaa = eris.ppaa[ncore:nocc,ncore:nocc,:,:]
aa11 = aa11 + aa11.transpose(2,3,0,1) - aaaa
a11a = numpy.dot(ra.T, paa1.reshape(nmo,-1)).reshape((ncas,)*4)
a11a = a11a + a11a.transpose(1,0,2,3)
a11a = a11a + a11a.transpose(0,1,3,2)
h2 = aa11 + a11a
jbuf = kbuf = p1aa = paa1 = aaaa = aa11 = a11a = None
# pure core response
# response of (1/2 dm * vhf * dm) ~ ddm*vhf
# Should I consider core response as a part of CI gradients?
ecore =(numpy.einsum('pq,pq->', h1e_mo, ddm) +
numpy.einsum('pq,pq->', eris.vhf_c, ddm))
### hessian_co part end ###
ci1, g = self.solve_approx_ci(h1, h2, fcivec, ecore, e_cas, envs)
if g is not None: # So state average CI, DMRG etc will not be applied
ovlp = numpy.dot(fcivec.ravel(), ci1.ravel())
norm_g = numpy.linalg.norm(g)
if 1-abs(ovlp) > norm_g * self.ci_grad_trust_region:
logger.debug(self, '<ci1|ci0>=%5.3g |g|=%5.3g, ci1 out of trust region',
ovlp, norm_g)
ci1 = fcivec.ravel() + g
ci1 *= 1/numpy.linalg.norm(ci1)
casdm1, casdm2 = self.fcisolver.make_rdm12(ci1, ncas, nelecas)
return casdm1, casdm2, g, ci1
[docs]
def solve_approx_ci(self, h1, h2, ci0, ecore, e_cas, envs):
''' Solve CI eigenvalue/response problem approximately
'''
ncas = self.ncas
nelecas = self.nelecas
if 'norm_gorb' in envs:
tol = max(self.conv_tol, envs['norm_gorb']**2*.1)
else:
tol = None
if getattr(self.fcisolver, 'approx_kernel', None):
fn = self.fcisolver.approx_kernel
e, ci1 = fn(h1, h2, ncas, nelecas, ecore=ecore, ci0=ci0,
tol=tol, max_memory=self.max_memory)
return ci1, None
elif not (getattr(self.fcisolver, 'contract_2e', None) and
getattr(self.fcisolver, 'absorb_h1e', None)):
fn = self.fcisolver.kernel
e, ci1 = fn(h1, h2, ncas, nelecas, ecore=ecore, ci0=ci0,
tol=tol, max_memory=self.max_memory,
max_cycle=self.ci_response_space)
return ci1, None
h2eff = self.fcisolver.absorb_h1e(h1, h2, ncas, nelecas, .5)
def contract_2e(c):
hc = self.fcisolver.contract_2e(h2eff, c, ncas, nelecas)
return hc.ravel()
hc = contract_2e(ci0)
g = hc - (e_cas-ecore) * ci0.ravel()
if self.ci_response_space > 7 or ci0.size <= self.fcisolver.pspace_size:
logger.debug(self, 'CI step by full response')
# full response
max_memory = max(400, self.max_memory-lib.current_memory()[0])
e, ci1 = self.fcisolver.kernel(h1, h2, ncas, nelecas, ecore=ecore,
ci0=ci0, tol=tol, max_memory=max_memory)
else:
nd = min(self.ci_response_space, ci0.size)
xs = [ci0.ravel()]
ax = [hc]
heff = numpy.empty((nd,nd))
seff = numpy.empty((nd,nd))
heff[0,0] = numpy.dot(xs[0], ax[0])
seff[0,0] = 1
tol_residual = self.fcisolver.conv_tol ** .5
for i in range(1, nd):
dx = ax[i-1] - xs[i-1] * e_cas
if numpy.linalg.norm(dx) < tol_residual:
break
xs.append(dx)
ax.append(contract_2e(xs[i]))
for j in range(i+1):
heff[i,j] = heff[j,i] = numpy.dot(xs[i], ax[j])
seff[i,j] = seff[j,i] = numpy.dot(xs[i], xs[j])
nd = len(xs)
e, v, seig = lib.safe_eigh(heff[:nd,:nd], seff[:nd,:nd])
ci1 = xs[0] * v[0,0]
for i in range(1, nd):
ci1 += xs[i] * v[i,0]
return ci1, g
[docs]
def get_grad(self, mo_coeff=None, casdm1_casdm2=None, eris=None):
'''Orbital gradients'''
if mo_coeff is None: mo_coeff = self.mo_coeff
if eris is None: eris = self.ao2mo(mo_coeff)
if casdm1_casdm2 is None:
e_tot, e_cas, civec = self.casci(mo_coeff, self.ci, eris)
casdm1, casdm2 = self.fcisolver.make_rdm12(civec, self.ncas, self.nelecas)
else:
casdm1, casdm2 = casdm1_casdm2
return self.gen_g_hop(mo_coeff, 1, casdm1, casdm2, eris)[0]
def _exact_paaa(self, mo, u, out=None):
nmo = mo.shape[1]
ncore = self.ncore
ncas = self.ncas
nocc = ncore + ncas
mo1 = numpy.dot(mo, u)
mo1_cas = mo1[:,ncore:nocc]
mos = (mo1_cas, mo1_cas, mo1, mo1_cas)
if self._scf._eri is None:
aapa = ao2mo.general(self.mol, mos)
else:
aapa = ao2mo.general(self._scf._eri, mos)
paaa = numpy.empty((nmo*ncas,ncas*ncas))
buf = numpy.empty((ncas,ncas,nmo*ncas))
for ij, (i, j) in enumerate(zip(*numpy.tril_indices(ncas))):
buf[i,j] = buf[j,i] = aapa[ij]
paaa = lib.transpose(buf.reshape(ncas*ncas,-1), out=out)
return paaa.reshape(nmo,ncas,ncas,ncas)
[docs]
def dump_chk(self, envs_or_file):
'''Serialize the MCSCF object and save it to the specified chkfile.
Args:
envs_or_file:
If this argument is a file path, the serialized MCSCF object is
saved to the file specified by this argument.
If this attribute is a dict (created by locals()), the necessary
variables are saved to the file specified by the attribute .chkfile.
'''
if isinstance(envs_or_file, str):
envs = None
chk_file = envs_or_file
else:
envs = envs_or_file
chk_file = self.chkfile
if not chk_file:
return self
e_tot = mo_coeff = mo_occ = mo_energy = e_cas = civec = casdm1 = None
ncore = self.ncore
nocc = ncore + self.ncas
if envs is not None:
if self.chk_ci:
civec = envs.get('fcivec', None)
e_tot = envs['e_tot']
e_cas = envs['e_cas']
casdm1 = envs['casdm1']
if 'mo' in envs:
mo_coeff = envs['mo']
else:
mo_coeff = envs['mo_coeff']
mo_occ = numpy.zeros(mo_coeff.shape[1])
mo_occ[:ncore] = 2
if self.natorb:
occ = self._eig(-casdm1, ncore, nocc)[0]
mo_occ[ncore:nocc] = -occ
else:
mo_occ[ncore:nocc] = casdm1.diagonal()
# Note: mo_energy in active space =/= F_{ii} (F is general Fock)
if 'mo_energy' in envs:
mo_energy = envs['mo_energy']
chkfile.dump_mcscf(self, chk_file, 'mcscf', e_tot,
mo_coeff, ncore, self.ncas, mo_occ,
mo_energy, e_cas, civec, casdm1,
overwrite_mol=(envs is None))
return self
[docs]
def update_from_chk(self, chkfile=None):
if chkfile is None: chkfile = self.chkfile
self.__dict__.update(lib.chkfile.load(chkfile, 'mcscf'))
return self
update = update_from_chk
[docs]
def rotate_mo(self, mo, u, log=None):
'''Rotate orbitals with the given unitary matrix'''
mo = numpy.dot(mo, u)
if log is not None and log.verbose >= logger.DEBUG:
ncore = self.ncore
ncas = self.ncas
nocc = ncore + ncas
s = reduce(numpy.dot, (mo[:,ncore:nocc].T, self._scf.get_ovlp(),
self.mo_coeff[:,ncore:nocc]))
log.debug('Active space overlap to initial guess, SVD = %s',
numpy.linalg.svd(s)[1])
log.debug('Active space overlap to last step, SVD = %s',
numpy.linalg.svd(u[ncore:nocc,ncore:nocc])[1])
return mo
[docs]
def micro_cycle_scheduler(self, envs):
if not WITH_MICRO_SCHEDULER:
return self.max_cycle_micro
log_norm_ddm = numpy.log(envs['norm_ddm'])
return max(self.max_cycle_micro, int(self.max_cycle_micro-1-log_norm_ddm))
max_stepsize_scheduler=max_stepsize_scheduler
[docs]
def ah_scheduler(self, envs):
pass
@property
def max_orb_stepsize(self): # pragma: no cover
return self.max_stepsize
@max_orb_stepsize.setter
def max_orb_stepsize(self, x): # pragma: no cover
sys.stderr.write('WARN: Attribute "max_orb_stepsize" was replaced by "max_stepsize"\n')
self.max_stepsize = x
@property
def ci_update_dep(self): # pragma: no cover
return self.with_dep4
@ci_update_dep.setter
def ci_update_dep(self, x): # pragma: no cover
sys.stderr.write('WARN: Attribute .ci_update_dep was replaced by .with_dep4 since PySCF v1.1.\n')
self.with_dep4 = x == 4
grad_update_dep = ci_update_dep
@property
def max_cycle(self):
return self.max_cycle_macro
@max_cycle.setter
def max_cycle(self, x):
self.max_cycle_macro = x
[docs]
def approx_hessian(self, auxbasis=None, with_df=None):
from pyscf.mcscf import df
return df.approx_hessian(self, auxbasis, with_df)
[docs]
def nuc_grad_method(self):
from pyscf.grad import casscf
return casscf.Gradients(self)
def _state_average_nuc_grad_method (self, state=None):
# Hook for addons.state_average. Every child method of CASSCF will
# probably need to overwrite this.
from pyscf.grad import sacasscf as sacasscf_grad
return sacasscf_grad.Gradients (self, state=state)
def _state_average_nac_method(self):
from pyscf.nac import sacasscf as sacasscf_nac
return sacasscf_nac.NonAdiabaticCouplings(self)
[docs]
def newton(self):
from pyscf.mcscf import newton_casscf
from pyscf.mcscf.addons import StateAverageMCSCFSolver
mc1 = newton_casscf.CASSCF(self._scf, self.ncas, self.nelecas)
mc1.__dict__.update(self.__dict__)
mc1.max_cycle_micro = 10
# MRH, 04/08/2019: enable state-average CASSCF second-order algorithm
if isinstance(self, StateAverageMCSCFSolver):
# FIXME: (QS) Should not need to pass wfnsym for general CASSCF object.
wfnsym = getattr(self, 'wfnsym', None)
mc1 = mc1.state_average_(self.weights, wfnsym)
return mc1
[docs]
def reset(self, mol=None):
casci.CASBase.reset(self, mol=mol)
self._max_stepsize = None
to_gpu = lib.to_gpu
scf.hf.RHF.CASSCF = scf.rohf.ROHF.CASSCF = lib.class_as_method(CASSCF)
scf.uhf.UHF.CASSCF = None
# to avoid calculating AO integrals
def _fake_h_for_fast_casci(casscf, mo, eris):
mc = casscf.view(CASCI)
mc.mo_coeff = mo
if eris is None:
return mc
ncore = casscf.ncore
nocc = ncore + casscf.ncas
mo_core = mo[:,:ncore]
mo_cas = mo[:,ncore:nocc]
core_dm = numpy.dot(mo_core, mo_core.T) * 2
hcore = casscf.get_hcore()
energy_core = casscf.energy_nuc()
energy_core += numpy.einsum('ij,ji', core_dm, hcore)
energy_core += eris.vhf_c[:ncore,:ncore].trace()
h1eff = reduce(numpy.dot, (mo_cas.T, hcore, mo_cas))
h1eff += eris.vhf_c[ncore:nocc,ncore:nocc]
mc.get_h1eff = lambda *args: (h1eff, energy_core)
eri_cas = eris.ppaa[ncore:nocc,ncore:nocc,:,:].copy()
mc.get_h2eff = lambda *args: eri_cas
return mc
[docs]
def expmat(a):
return scipy.linalg.expm(a)
if __name__ == '__main__':
from pyscf import scf
from pyscf import fci
from pyscf.mcscf import addons
mol = gto.Mole()
mol.verbose = 0
mol.output = None#"out_h2o"
mol.atom = [
['H', ( 1.,-1. , 0. )],
['H', ( 0.,-1. ,-1. )],
['H', ( 1.,-0.5 ,-1. )],
['H', ( 0.,-0.5 ,-1. )],
['H', ( 0.,-0.5 ,-0. )],
['H', ( 0.,-0. ,-1. )],
['H', ( 1.,-0.5 , 0. )],
['H', ( 0., 1. , 1. )],
]
mol.basis = {'H': 'sto-3g',
'O': '6-31g',}
mol.build()
m = scf.RHF(mol)
ehf = m.scf()
emc = kernel(CASSCF(m, 4, 4), m.mo_coeff, verbose=4)[1]
print(ehf, emc, emc-ehf)
print(emc - -3.22013929407)
mc = CASSCF(m, 4, (3,1))
mc.verbose = 4
#mc.fcisolver = fci.direct_spin1
mc.fcisolver = fci.solver(mol, False)
emc = kernel(mc, m.mo_coeff, verbose=4)[1]
print(emc - -15.950852049859-mol.energy_nuc())
mol.atom = [
['H', ( 5.,-1. , 1. )],
['H', ( 0.,-5. ,-2. )],
['H', ( 4.,-0.5 ,-3. )],
['H', ( 0.,-4.5 ,-1. )],
['H', ( 3.,-0.5 ,-0. )],
['H', ( 0.,-3. ,-1. )],
['H', ( 2.,-2.5 , 0. )],
['H', ( 1., 1. , 3. )],
]
mol.basis = {'H': 'sto-3g',
'O': '6-31g',}
mol.build()
m = scf.RHF(mol)
ehf = m.scf()
emc = kernel(CASSCF(m, 4, 4), m.mo_coeff, verbose=4)[1]
print(ehf, emc, emc-ehf)
print(emc - -3.62638367550087, emc - -3.6268060528596635)
mc = CASSCF(m, 4, (3,1))
mc.verbose = 4
mc.natorb = 1
#mc.fcisolver = fci.direct_spin1
mc.fcisolver = fci.solver(mol, False)
emc = kernel(mc, m.mo_coeff, verbose=4)[1]
print(emc - -3.62638367550087)
mol.atom = [
['O', ( 0., 0. , 0. )],
['H', ( 0., -0.757, 0.587)],
['H', ( 0., 0.757 , 0.587)],]
mol.basis = {'H': 'cc-pvdz',
'O': 'cc-pvdz',}
mol.build()
m = scf.RHF(mol)
ehf = m.scf()
mc = CASSCF(m, 6, 4)
mc.fcisolver = fci.solver(mol)
mc.verbose = 4
mo = addons.sort_mo(mc, m.mo_coeff, (3,4,6,7,8,9), 1)
emc = mc.mc1step(mo)[0]
print(ehf, emc, emc-ehf)
#-76.0267656731 -76.0873922924 -0.0606266193028
print(emc - -76.0873923174, emc - -76.0926176464)
mc = CASSCF(m, 6, (3,1))
mo = addons.sort_mo(mc, m.mo_coeff, (3,4,6,7,8,9), 1)
#mc.fcisolver = fci.direct_spin1
mc.fcisolver = fci.solver(mol, False)
mc.verbose = 4
emc = mc.mc1step(mo)[0]
#mc.analyze()
print(emc - -75.7155632535814)
mc.internal_rotation = True
emc = mc.mc1step(mo)[0]
print(emc - -75.7155632535814)
