14. cc — Coupled cluster¶
The cc
module implements the coupled cluster (CC) model to compute
energies, analytical nuclear gradients, density matrices, excited states, and
relevant properties.
To compute the CC energy, one first needs to perform a meanfield calculation using
the meanfield module scf
. The meanfield object defines the Hamiltonian
and the problem size, which are used to initialize the CC object:
from pyscf import gto, scf, cc
mol = gto.M(atom='H 0 0 0; F 0 0 1', basis='ccpvdz')
mf = scf.RHF(mol).run()
mycc = cc.CCSD(mf)
mycc.kernel()
Unrelaxed density matrices are evaluated in the MO basis:
dm1 = mycc.make_rdm1()
dm2 = mycc.make_rdm2()
The CCSD(T) energy can be obtained by:
from pyscf.cc import ccsd_t
print(ccsd_t.kernel(mycc, mycc.ao2mo())[0])
Gradients are available:
from pyscf.cc import ccsd_grad
from pyscf import grad
grad_e = ccsd_grad.kernel(mycc)
grad_n = grad.grad_nuc(mol)
grad = grad_e + grad_nuc
Excited states can be calculated with ionization potential (IP), electron affinity (EA), and electronic excitation (EE) equationofmotion (EOM) CCSD:
mycc = cc.RCCSD(mf)
mycc.kernel()
e_ip, c_ip = mycc.ipccsd(nroots=1)
e_ea, c_ea = mycc.eaccsd(nroots=1)
e_ee, c_ee = mycc.eeccsd(nroots=1)
mycc = cc.UCCSD(mf)
mycc.kernel()
e_ip, c_ip = mycc.ipccsd(nroots=1)
e_ea, c_ea = mycc.eaccsd(nroots=1)
e_ee, c_ee = mycc.eeccsd(nroots=1)
All CC methods have two implementations. One is simple and highly readable (suffixed
by _slow
in the filename) and the other is extensively optimized for
computational efficiency.
All code in the _slow
versions is structured as close as
possible to the formulas documented in the literature. Pure Python/numpy
data structures and functions are used so that explicit memory management is avoided.
It is easy to make modifications or develop new methods based on the slow
implementations.
The computationally efficient (outcore) version is the default implementation
for the CC module. In this implementation, the CPU usage, memory footprint,
memory efficiency, and IO overhead are carefully considered. To keep a small
memory footprint, most integral tensors are stored on disk. IO is one of the
main bottlenecks in this implementation. Two techniques are used to reduce
the IO overhead. One is the asynchronized IO to overlap the computation and
reading/writing of the 4index tensors. The other is AOdriven for the
contraction of T2 and (vvvv)
integrals in CCSD and CCSDlambda functions.
These techniques allow the CC module to efficiently handle mediumsized
systems. In a test system with 25 occupied orbitals and 1500 virtual orbitals, each
CCSD iteration takes about 2.5 hours. The program does not automatically
switch to AOdriven CCSD for large systems. The user must manually set the
direct
attribute to enable an AOdriven CCSD calculation:
mycc = cc.CCSD(mf)
mycc.direct = True
mycc.kernel()
Some of the CC methods have an efficient incore implementation, where all tensors are held in memory. The incore implementation reduces the IO overhead and optimizes certain formulas to gain the best FLOPS. It is about 30% faster than the outcore implementation. Depending on the available memory, the incore code can be used for systems with up to approximately 250 orbitals.
Point group symmetry is not considered in the CCSD programs, but it is used in the CCSD(T) code to gain the best performance.
Arbitrary frozen orbitals (not limited to frozen core) are supported by the CCSD, CCSD(T), density matrices, and EOMCCSD modules, but not in the analytical CCSD gradient module.
14.1. Examples¶
This section documents some examples about how to effectively use the CCSD
module, and how to incorporate the CCSD solver with other PySCF functions to
perform advanced simulations. For a complete list of CC examples, see
pyscf/examples/cc
.
14.1.1. A general solver for customized Hamiltonian¶
The CC module is not limited to molecular systems. The program is implemented as a general solver for arbitrary Hamiltonians. It allows users to overwrite the default molecular Hamiltonian with their own effective Hamiltonians. In this example, we create a Hubbard model and feed its Hamiltonian to the CCSD module.
#!/usr/bin/env python
'''
Sixsite 1D U/t=2 Hubbardlike model system with PBC at half filling.
The model is gapped at the meanfield level
'''
import numpy
from pyscf import gto, scf, ao2mo, cc
mol = gto.M(verbose=4)
n = 6
mol.nelectron = n
# Setting incore_anyway=True to ensure the customized Hamiltonian (the _eri
# attribute) to be used in the postHF calculations. Without this parameter,
# some postHF method (particularly in the MO integral transformation) may
# ignore the customized Hamiltonian if memory is not enough.
mol.incore_anyway = True
h1 = numpy.zeros((n,n))
for i in range(n1):
h1[i,i+1] = h1[i+1,i] = 1.0
h1[n1,0] = h1[0,n1] = 1.0
eri = numpy.zeros((n,n,n,n))
for i in range(n):
eri[i,i,i,i] = 2.0
mf = scf.RHF(mol)
mf.get_hcore = lambda *args: h1
mf.get_ovlp = lambda *args: numpy.eye(n)
mf._eri = ao2mo.restore(8, eri, n)
mf.kernel()
# In PySCF, the customized Hamiltonian needs to be created once in mf object.
# The Hamiltonian will be used everywhere whenever possible. Here, the model
# Hamiltonian is passed to CCSD object via the mf object.
mycc = cc.RCCSD(mf)
mycc.kernel()
e,v = mycc.ipccsd(nroots=3)
print(e)
14.1.2. Using CCSD as CASCI active space solver¶
CCSD program can be wrapped as a Full CI solver, which can be combined with the CASCI solver to approximate the multiconfiguration calculation.
#!/usr/bin/env python
'''
Using the CCSD method as the active space solver to compute an approximate
CASCI energy.
A wrapper is required to adapt the CCSD solver to CASCI fcisolver interface.
Inside the wrapper function, the CCSD code is the same as the example
40ccsd_with_given_hamiltonian.py
'''
import numpy
from pyscf import gto, scf, cc, ao2mo, mcscf
class AsFCISolver(object):
def __init__(self):
self.mycc = None
def kernel(self, h1, h2, norb, nelec, ci0=None, ecore=0, **kwargs):
fakemol = gto.M(verbose=0)
nelec = numpy.sum(nelec)
fakemol.nelectron = nelec
fake_hf = scf.RHF(fakemol)
fake_hf._eri = ao2mo.restore(8, h2, norb)
fake_hf.get_hcore = lambda *args: h1
fake_hf.get_ovlp = lambda *args: numpy.eye(norb)
fake_hf.kernel()
self.mycc = cc.CCSD(fake_hf)
self.eris = self.mycc.ao2mo()
e_corr, t1, t2 = self.mycc.kernel(eris=self.eris)
l1, l2 = self.mycc.solve_lambda(t1, t2, eris=self.eris)
e = fake_hf.e_tot + e_corr
return e+ecore, [t1,t2,l1,l2]
def make_rdm1(self, fake_ci, norb, nelec):
mo = self.mycc.mo_coeff
t1, t2, l1, l2 = fake_ci
dm1 = reduce(numpy.dot, (mo, self.mycc.make_rdm1(t1, t2, l1, l2), mo.T))
return dm1
def make_rdm12(self, fake_ci, norb, nelec):
mo = self.mycc.mo_coeff
nmo = mo.shape[1]
t1, t2, l1, l2 = fake_ci
dm2 = self.mycc.make_rdm2(t1, t2, l1, l2)
dm2 = numpy.dot(mo, dm2.reshape(nmo,1))
dm2 = numpy.dot(dm2.reshape(1,nmo), mo.T)
dm2 = dm2.reshape([nmo]*4).transpose(2,3,0,1)
dm2 = numpy.dot(mo, dm2.reshape(nmo,1))
dm2 = numpy.dot(dm2.reshape(1,nmo), mo.T)
dm2 = dm2.reshape([nmo]*4)
return self.make_rdm1(fake_ci, norb, nelec), dm2
def spin_square(self, fake_ci, norb, nelec):
return 0, 1
mol = gto.M(atom = 'H 0 0 0; F 0 0 1.2',
basis = 'ccpvdz',
verbose = 4)
mf = scf.RHF(mol).run()
norb = mf.mo_coeff.shape[1]
nelec = mol.nelectron
mc = mcscf.CASCI(mf, norb, nelec)
mc.fcisolver = AsFCISolver()
mc.kernel()
14.1.3. Gamma point CCSD with Periodic boundary condition¶
Integrals in Gamma point of periodic HartreeFock calculation are all real. You can feed the integrals into any pyscf molecular module using the same operations as the above example. However, the interface between PBC code and molecular code are more compatible. You can treat the crystal object and the molecule object in the same manner. In this example, you can pass the PBC mean field method to CC module to have the gamma point CCSD correlation.
#!/usr/bin/env python
'''
Gamma point postHF calculation needs only real integrals.
Methods implemented in finitesize system can be directly used here without
any modification.
'''
import numpy
from pyscf.pbc import gto, scf
cell = gto.M(
a = numpy.eye(3)*3.5668,
atom = '''C 0. 0. 0.
C 0.8917 0.8917 0.8917
C 1.7834 1.7834 0.
C 2.6751 2.6751 0.8917
C 1.7834 0. 1.7834
C 2.6751 0.8917 2.6751
C 0. 1.7834 1.7834
C 0.8917 2.6751 2.6751''',
basis = '631g',
verbose = 4,
)
mf = scf.RHF(cell).density_fit()
mf.with_df.mesh = [10]*3
mf.kernel()
#
# Import CC, TDDFT moduel from the molecular implementations
#
from pyscf import cc, tddft
mycc = cc.CCSD(mf)
mycc.kernel()
mytd = tddft.TDHF(mf)
mytd.nstates = 5
mytd.kernel()
14.1.4. CCSD with truncated MOs to avoid linear dependency¶
It is common to have linear dependence when one wants to systematically enlarge the AO basis set to approach complete basis set limit. The numerical instability usually has noticeable effects on the CCSD convergence. An effective way to remove this negative effects is to truncate the AO sets and allow the MO orbitals being less than AO functions.
#!/usr/bin/env python
'''
:func:`scf.addons.remove_linear_dep_` discards the small eigenvalues of overlap
matrix. This reduces the number of MOs from 50 to 49. The problem size of
the following CCSD method is 49.
'''
from pyscf import gto, scf, cc
mol = gto.Mole()
mol.atom = [('H', 0, 0, .5*i) for i in range(20)]
mol.basis = 'ccpvdz'
mol.verbose = 4
mol.build()
mf = scf.RHF(mol).run()
mycc = cc.CCSD(mf).run()
mf = scf.addons.remove_linear_dep_(mf).run()
mycc = cc.CCSD(mf).run()
14.1.5. Response and unrelaxed CCSD density matrix¶
CCSD has two kinds of oneparticle density matrices. The (second order)
unrelaxed density matrix and the (relaxed) response density matrix. The
CCSD.make_rdm1()
function computes the unrelaxed density matrix which is
associated to the regular CCSD energy formula. The response density is mainly
used to compute the first order response quantities eg the analytical nuclear
gradients. It is not recommended to use the response density matrix for
population analysis.
#!/usr/bin/env python
#
# Author: Qiming Sun <osirpt.sun@gmail.com>
#
'''
CCSD density matrix
'''
import numpy
from pyscf import gto, scf, cc, ao2mo
mol = gto.M(
atom = 'H 0 0 0; F 0 0 1.1',
basis = 'ccpvdz')
mf = scf.RHF(mol).run()
mycc = cc.CCSD(mf).run()
#
# CCSD density matrix in MO basis
#
dm1 = mycc.make_rdm1()
dm2 = mycc.make_rdm2()
#
# CCSD energy based on density matrices
#
h1 = numpy.einsum('pi,pq,qj>ij', mf.mo_coeff.conj(), mf.get_hcore(), mf.mo_coeff)
nmo = mf.mo_coeff.shape[1]
eri = ao2mo.kernel(mol, mf.mo_coeff, compact=False).reshape([nmo]*4)
E = numpy.einsum('pq,qp', h1, dm1)
# Note dm2 is transposed to simplify its contraction to integrals
E+= numpy.einsum('pqrs,pqrs', eri, dm2) * .5
E+= mol.energy_nuc()
print('E(CCSD) = %s, reference %s' % (E, mycc.e_tot))
# When plotting CCSD density on the grids, CCSD density matrices need to be
# first transformed to AO basis.
dm1_ao = numpy.einsum('pi,ij,qj>pq', mf.mo_coeff, dm1, mf.mo_coeff.conj())
from pyscf.tools import cubegen
cubegen.density(mol, 'rho_ccsd.cube', dm1_ao)
14.1.6. Reusing integrals in CCSD and relevant calculations¶
By default the CCSD solver and the relevant CCSD lambda solver, CCSD(T), CCSD gradients program generate MO integrals in their own runtime. But in most scenario, the same MO integrals can be generated once and reused in the four modules. To remove the overhead of recomputing MO integrals, the three module support user to feed MO integrals.
#!/usr/bin/env python
#
# Author: Qiming Sun <osirpt.sun@gmail.com>
#
'''
To avoid recomputing AO to MO integral transformation, integrals for CCSD,
CCSD(T), CCSD lambda equation etc can be reused.
'''
from pyscf import gto, scf, cc
mol = gto.M(verbose = 4,
atom = 'H 0 0 0; F 0 0 1.1',
basis = 'ccpvdz')
mf = scf.RHF(mol).run()
mycc = cc.CCSD(mf)
#
# CCSD module allows you feed MO integrals
#
eris = mycc.ao2mo()
mycc.kernel(eris=eris)
#
# The same MO integrals can be used in CCSD lambda equation
#
mycc.solve_lambda(eris=eris)
#
# CCSD(T) module requires the same integrals used by CCSD module
#
from pyscf.cc import ccsd_t
ccsd_t.kernel(mycc, eris=eris)
#
# CCSD gradients need regular MO integrals to solve the relaxed 1particle
# density matrix
#
from pyscf.cc import ccsd_grad
grad_e = ccsd_grad.kernel(mycc, eris=eris) # The electronic part only
14.1.7. Interfering CCSDDIIS¶
14.1.8. Restart CCSD¶
14.2. Program reference¶
14.2.1. cc.ccsd module and CCSD class¶
The pyscf.cc.ccsd.CCSD
class is the object to hold the restricted CCSD environment
attributes and results. The environment attributes are the parameters to
control the runtime behavior of the CCSD module, e.g. the convergence criteria, DIIS
parameters, and so on. After the ground state CCSD calculation, correlation
energy, T1
and T2
amplitudes are stored in the CCSD object.
This class supports the calculation of CCSD 1 and 2particle density matrices.

class
pyscf.cc.ccsd.
CCSD
(mf, frozen=0, mo_coeff=None, mo_occ=None)[source]¶ restricted CCSD
 Attributes:
 verbose : int
 Print level. Default value equals to
Mole.verbose
 max_memory : float or int
 Allowed memory in MB. Default value equals to
Mole.max_memory
 conv_tol : float
 converge threshold. Default is 1e7.
 conv_tol_normt : float
 converge threshold for norm(t1,t2). Default is 1e5.
 max_cycle : int
 max number of iterations. Default is 50.
 diis_space : int
 DIIS space size. Default is 6.
 diis_start_cycle : int
 The step to start DIIS. Default is 0.
 iterative_damping : float
 The self consistent damping parameter.
 direct : bool
 AOdirect CCSD. Default is False.
 async_io : bool
 Allow for asynchronous function execution. Default is True.
 incore_complete : bool
 Avoid all I/O (also for DIIS). Default is False.
 level_shift : float
 A shift on virtual orbital energies to stablize the CCSD iteration
 frozen : int or list
If integer is given, the innermost orbitals are frozen from CC amplitudes. Given the orbital indices (0based) in a list, both occupied and virtual orbitals can be frozen in CC calculation.
>>> mol = gto.M(atom = 'H 0 0 0; F 0 0 1.1', basis = 'ccpvdz') >>> mf = scf.RHF(mol).run() >>> # freeze 2 core orbitals >>> mycc = cc.CCSD(mf).set(frozen = 2).run() >>> # freeze 2 core orbitals and 3 high lying unoccupied orbitals >>> mycc.set(frozen = [0,1,16,17,18]).run()
Saved results
 converged : bool
 CCSD converged or not
 e_corr : float
 CCSD correlation correction
 e_tot : float
 Total CCSD energy (HF + correlation)
 t1, t2 :
 T amplitudes t1[i,a], t2[i,j,a,b] (i,j in occ, a,b in virt)
 l1, l2 :
 Lambda amplitudes l1[i,a], l2[i,j,a,b] (i,j in occ, a,b in virt)
RCCSD for real integrals 8fold permutation symmetry has been used (ijkl) = (jikl) = (klij) = ...

class
pyscf.cc.ccsd.
CCSD
(mf, frozen=0, mo_coeff=None, mo_occ=None)[source] restricted CCSD
 Attributes:
 verbose : int
 Print level. Default value equals to
Mole.verbose
 max_memory : float or int
 Allowed memory in MB. Default value equals to
Mole.max_memory
 conv_tol : float
 converge threshold. Default is 1e7.
 conv_tol_normt : float
 converge threshold for norm(t1,t2). Default is 1e5.
 max_cycle : int
 max number of iterations. Default is 50.
 diis_space : int
 DIIS space size. Default is 6.
 diis_start_cycle : int
 The step to start DIIS. Default is 0.
 iterative_damping : float
 The self consistent damping parameter.
 direct : bool
 AOdirect CCSD. Default is False.
 async_io : bool
 Allow for asynchronous function execution. Default is True.
 incore_complete : bool
 Avoid all I/O (also for DIIS). Default is False.
 level_shift : float
 A shift on virtual orbital energies to stablize the CCSD iteration
 frozen : int or list
If integer is given, the innermost orbitals are frozen from CC amplitudes. Given the orbital indices (0based) in a list, both occupied and virtual orbitals can be frozen in CC calculation.
>>> mol = gto.M(atom = 'H 0 0 0; F 0 0 1.1', basis = 'ccpvdz') >>> mf = scf.RHF(mol).run() >>> # freeze 2 core orbitals >>> mycc = cc.CCSD(mf).set(frozen = 2).run() >>> # freeze 2 core orbitals and 3 high lying unoccupied orbitals >>> mycc.set(frozen = [0,1,16,17,18]).run()
Saved results
 converged : bool
 CCSD converged or not
 e_corr : float
 CCSD correlation correction
 e_tot : float
 Total CCSD energy (HF + correlation)
 t1, t2 :
 T amplitudes t1[i,a], t2[i,j,a,b] (i,j in occ, a,b in virt)
 l1, l2 :
 Lambda amplitudes l1[i,a], l2[i,j,a,b] (i,j in occ, a,b in virt)

as_scanner
(cc)¶ Generating a scanner/solver for CCSD PES.
The returned solver is a function. This function requires one argument “mol” as input and returns total CCSD energy.
The solver will automatically use the results of last calculation as the initial guess of the new calculation. All parameters assigned in the CCSD and the underlying SCF objects (conv_tol, max_memory etc) are automatically applied in the solver.
Note scanner has side effects. It may change many underlying objects (_scf, with_df, with_x2c, ...) during calculation.
Examples:
>>> from pyscf import gto, scf, cc >>> mol = gto.M(atom='H 0 0 0; F 0 0 1') >>> cc_scanner = cc.CCSD(scf.RHF(mol)).as_scanner() >>> e_tot = cc_scanner(gto.M(atom='H 0 0 0; F 0 0 1.1')) >>> e_tot = cc_scanner(gto.M(atom='H 0 0 0; F 0 0 1.5'))

energy
(mycc, t1=None, t2=None, eris=None)¶ CCSD correlation energy

get_frozen_mask
(mp)¶ Get boolean mask for the restricted reference orbitals.
In the returned boolean (mask) array of frozen orbital indices, the element is False if it corresonds to the frozen orbital.

make_rdm1
(t1=None, t2=None, l1=None, l2=None)[source]¶ Unrelaxed 1particle density matrix in MO space

make_rdm2
(t1=None, t2=None, l1=None, l2=None)[source]¶ 2particle density matrix in MO space. The density matrix is stored as
dm2[p,r,q,s] = <p^+ q^+ s r>

restore_from_diis_
(mycc, diis_file, inplace=True)¶ Reuse an existed DIIS object in the CCSD calculation.
The CCSD amplitudes will be restored from the DIIS object to generate t1 and t2 amplitudes. The t1/t2 amplitudes of the CCSD object will be overwritten by the generated t1 and t2 amplitudes. The amplitudes vector and error vector will be reused in the CCSD calculation.

pyscf.cc.ccsd.
as_scanner
(cc)[source]¶ Generating a scanner/solver for CCSD PES.
The returned solver is a function. This function requires one argument “mol” as input and returns total CCSD energy.
The solver will automatically use the results of last calculation as the initial guess of the new calculation. All parameters assigned in the CCSD and the underlying SCF objects (conv_tol, max_memory etc) are automatically applied in the solver.
Note scanner has side effects. It may change many underlying objects (_scf, with_df, with_x2c, ...) during calculation.
Examples:
>>> from pyscf import gto, scf, cc >>> mol = gto.M(atom='H 0 0 0; F 0 0 1') >>> cc_scanner = cc.CCSD(scf.RHF(mol)).as_scanner() >>> e_tot = cc_scanner(gto.M(atom='H 0 0 0; F 0 0 1.1')) >>> e_tot = cc_scanner(gto.M(atom='H 0 0 0; F 0 0 1.5'))

pyscf.cc.ccsd.
restore_from_diis_
(mycc, diis_file, inplace=True)[source]¶ Reuse an existed DIIS object in the CCSD calculation.
The CCSD amplitudes will be restored from the DIIS object to generate t1 and t2 amplitudes. The t1/t2 amplitudes of the CCSD object will be overwritten by the generated t1 and t2 amplitudes. The amplitudes vector and error vector will be reused in the CCSD calculation.
14.2.2. cc.rccsd and RCCSD class¶
pyscf.cc.rccsd.RCCSD
is also a class for restricted CCSD calculations, but
different to the pyscf.cc.ccsd.CCSD
class. It uses different formula
to compute the ground state CCSD solution. Although slower than the
implmentation in the pyscf.cc.ccsd.CCSD
class, it supports the system
with complex integrals. Another difference is that this class supports EOMCCSD
methods, including EOMIPCCSD, EOMEACCSD, EOMEECCSD, EOMSFCCSD.

class
pyscf.cc.rccsd.
RCCSD
(mf, frozen=0, mo_coeff=None, mo_occ=None)[source]¶ restricted CCSD with IPEOM, EAEOM, EEEOM, and SFEOM capabilities
Groundstate CCSD is performed in optimized ccsd.CCSD and EOM is performed here.
Restricted CCSD implementation which supports both real and complex integrals. The 4index integrals are saved on disk entirely (without using any symmetry). This code is slower than the pyscf.cc.ccsd implementation.
Note MO integrals are treated in chemist’s notation
Ref: Hirata et al., J. Chem. Phys. 120, 2581 (2004)

class
pyscf.cc.rccsd.
RCCSD
(mf, frozen=0, mo_coeff=None, mo_occ=None)[source] restricted CCSD with IPEOM, EAEOM, EEEOM, and SFEOM capabilities
Groundstate CCSD is performed in optimized ccsd.CCSD and EOM is performed here.

ccsd
(t1=None, t2=None, eris=None, mbpt2=False)[source]¶ Groundstate CCSD.
 Kwargs:
 mbpt2 : bool
 Use oneshot MBPT2 approximation to CCSD.

energy
(cc, t1=None, t2=None, eris=None)¶ RCCSD correlation energy

14.2.3. cc.uccsd and UCCSD class¶
pyscf.cc.uccsd.UCCSD
class supports the CCSD calculation based on UHF
wavefunction as well as the ROHF wavefunction. Besides the ground state UCCSD calculation,
UCCSD lambda equation, 1particle and 2particle density matrices, EOMIPCCSD,
EOMEACCSD, EOMEECCSD are all available in this class. Note this class does
not support complex integrals.
UCCSD with spatial integrals
14.2.4. cc.addons¶
Helper functions for CCSD, RCCSD and UCCSD modules are implemented in
cc.addons

pyscf.cc.addons.
spatial2spin
(tx, orbspin=None)[source]¶ Convert T1/T2 of spatial orbital representation to T1/T2 of spinorbital representation

pyscf.cc.addons.
spatial2spinorb
(tx, orbspin=None)¶ Convert T1/T2 of spatial orbital representation to T1/T2 of spinorbital representation
14.2.5. CCSD(T)¶
RHFCCSD(T) for real integrals
14.2.6. CCSD gradients¶

pyscf.cc.ccsd_grad.
as_scanner
(cc)¶ Generating a scanner/solver for CCSD PES.
The returned solver is a function. This function requires one argument “mol” as input and returns total CCSD energy.
The solver will automatically use the results of last calculation as the initial guess of the new calculation. All parameters assigned in the CCSD and the underlying SCF objects (conv_tol, max_memory etc) are automatically applied in the solver.
Note scanner has side effects. It may change many underlying objects (_scf, with_df, with_x2c, ...) during calculation.
Examples:
>>> from pyscf import gto, scf, cc >>> mol = gto.M(atom='H 0 0 0; F 0 0 1') >>> cc_scanner = cc.CCSD(scf.RHF(mol)).as_scanner() >>> e_tot, grad = cc_scanner(gto.M(atom='H 0 0 0; F 0 0 1.1')) >>> e_tot, grad = cc_scanner(gto.M(atom='H 0 0 0; F 0 0 1.5'))