coupled-cluster-py 0.0.4

Coupled-cluster package written in Python.

CCpy: A coupled-cluster package written in Python.

Overview

CCpy is a research-level Python package for performing non-relativistic electronic structure calculations for molecular systems using methods based on the ground-state coupled-cluster (CC) theory and its equation-of-motion (EOM) extension to electronic excited, attached, and ionized states. As a design philosophy, CCpy favors simplicity over efficiency, and this is reflected in the usage of computational routines that are transparent enough so that they can be easily used, modified, and extended, while still maintaining reasonable efficiency. To this end, CCpy employs a hybrid Python-Fortran programming approach made possible with the f2py package, which allows one to compile Fortran code into shared object libraries containing subroutines that are callable from Python and interoperable with Numpy arrays.

Available Computational Options

CCpy specializes in applying the CC(P;Q) and externally corrected (ec) CC methodologies developed in the Piecuch group at Michigan State University. In CC(P;Q), the energetics obtained by solving the ground- or excited-state CC/EOMCC equations in one subspace of the many-electron Hilbert space, called the P space, are corrected for the missing many-electron correlation effects captured with the help of a complementary subspace called the Q space using the state-selective, non-iterative, and non-perturbative energy corrections based on the CC moment expansion formalism. Currently, CCpy offers implementations of several CC(P;Q) methods, the majority of which are aimed at converging the high-level CCSDT and EOMCCSDT energetics. These include the completely-renormalized (CR) methods such as the CR-CC(2,3) and CR-CC(2,4) triples and quadruples corrections to CCSD, the active-space CCSDt and CC(t;3) approaches, which are based on a user-defined selection of active orbitals, and the black-box selected configuration interaction (CI) driven and adaptive CC(P;Q) methodologies, which construct the P and Q spaces entering the CC(P;Q) computations using information extracted from selected CI wave functions or the adaptive CC(P;Q) moment expansions themselves, respectively. The ec-CC approaches on the other hand seek to converge the exact, full CI energetics directly by solving for the T₁ and T₂ clusters in the presence of the leading T₃ and T₄ clusters extracted from an external non-CC wave function. Current implementations of the ec-CC approaches in CCpy are designed to iterate T₁ and T₂ clusters in the presence of T₃ and T₄ obtained from CI wave functions of the selected CI or multireference CI types, and correct the resulting energetics for the missing many-electron correlations using the generalized moment expansions of the ec-CC equations.

Møller-Plesset (MP) perturbation theory

• MP2
• MP3

Ground-state CC methodologies

CCD

Summary

The CC with doubles (CCD) method truncates the cluster operator as T = T₂. It has iterative computational costs that scale as n_o²n_u⁴, where n_o is the number of correlated occupied orbitals and n_u is the number of correlated unoccupied orbitals. Due to the importance of pair correlations in the many-electron problem, the CCD approximation was first introduced in Prof. Čížek's landmark 1966 paper under the name coupled-pair many-electron theory, or CPMET. Although CCD is often superceeded by the more accurate CC with singles and doubles (CCSD) method, which has the same computational scaling, CCD is still relevant to modern CC calculations within the context of correlating orbital-optimized reference functions, as in Brückner CCD.

Example Code

    from pyscf import gto, scf
    from ccpy.drivers.driver import Driver

    # build molecule using PySCF and run SCF calculation
    mol = gto.M(
        atom=[["O", (0.0, 0.0, -0.0180)],
              ["H", (0.0, 3.030526, -2.117796)],
              ["H", (0.0, -3.030526, -2.117796)]],
        basis="cc-pvdz",
        charge=0,
        spin=0,
        symmetry="C2V",
        cart=False,
        unit="Bohr",
    )
    mf = scf.RHF(mol)
    mf.kernel()
    
    # get the CCpy driver object using PySCF meanfield
    driver = Driver.from_pyscf(mf, nfrozen=1)

    # set calculation parameters
    driver.options["energy_convergence"] = 1.0e-07 # (in hartree)
    driver.options["amp_convergence"] = 1.0e-07
    driver.options["maximum_iterations"] = 80

    # run CCD calculation
    driver.run_cc(method="ccd")

Reference

1. J. Čížek, J. Chem. Phys. 45, 4256 (1966).

CCSD

Summary

The CC with singles and doubles (CCSD) method approximates the cluster operator as T = T₁ + T₂. It is the most commonly used truncation level in the CC hierarchy and often forms the starting point for more sophisticated treatments of many-electron correlation effects. CCSD has iterative computational costs that scale as n_o²n_u⁴, where n_o is the number of correlated occupied orbitals and n_u is the number of correlated unoccupied orbitals.

Sample Code

    from pyscf import gto, scf
    from ccpy.drivers.driver import Driver

    # build molecule using PySCF and run SCF calculation
    mol = gto.M(
        atom=[["O", (0.0, 0.0, -0.0180)],
              ["H", (0.0, 3.030526, -2.117796)],
              ["H", (0.0, -3.030526, -2.117796)]],
        basis="cc-pvdz",
        charge=0,
        spin=0,
        symmetry="C2V",
        cart=False,
        unit="Bohr",
    )
    mf = scf.RHF(mol)
    mf.kernel()
    
    # get the CCpy driver object using PySCF meanfield
    driver = Driver.from_pyscf(mf, nfrozen=1)

    # set calculation parameters
    driver.options["energy_convergence"] = 1.0e-07 # (in hartree)
    driver.options["amp_convergence"] = 1.0e-07
    driver.options["maximum_iterations"] = 80

    # run CCSD calculation
    driver.run_cc(method="ccsd")

References

1. G. D. Purvis and R. J. Bartlett, J. Chem. Phys. 76, 1910 (1982).
2. J. M. Cullen and M. C. Zerner, J. Chem. Phys. 77, 4088 (1982).
3. G. E. Scuseria, A. C. Scheiner, T. J. Lee, J. E. Rice, and H. F. Schaefer, J. Chem. Phys. 86, 2881 (1987).
4. P. Piecuch and J. Paldus, Int. J. Quantum Chem. 36, 429 (1989).

CCSDT

Summary

The CC with singles, doubles, and triples (CCSDT) method approximates the cluster operator as T = T₁ + T₂ + T₃. CCSDT is a high-level method capable of providing nearly exact results for closed-shell molecules as well as chemically accurate energetics for single bond breaking and a variety of open-shell systems. CCSDT has iterative computational costs that scale as n_o³n_u⁵, where n_o is the number of correlated occupied orbitals and n_u is the number of correlated unoccupied orbitals.

Sample Code

    from pyscf import gto, scf
    from ccpy.drivers.driver import Driver

    # build molecule using PySCF and run SCF calculation
    mol = gto.M(
        atom=[["O", (0.0, 0.0, -0.0180)],
              ["H", (0.0, 3.030526, -2.117796)],
              ["H", (0.0, -3.030526, -2.117796)]],
        basis="cc-pvdz",
        charge=0,
        spin=0,
        symmetry="C2V",
        cart=False,
        unit="Bohr",
    )
    mf = scf.RHF(mol)
    mf.kernel()
    
    # get the CCpy driver object using PySCF meanfield
    driver = Driver.from_pyscf(mf, nfrozen=1)

    # set calculation parameters
    driver.options["energy_convergence"] = 1.0e-07 # (in hartree)
    driver.options["amp_convergence"] = 1.0e-07
    driver.options["maximum_iterations"] = 80

    # run CCSDT calculation
    driver.run_cc(method="ccsdt")

References

1. M. R. Hoffmann and H. F. Schaefer, Adv. Quantum Chem. 18, 207 (1986).
2. J. Noga and R. J. Bartlett, J. Chem. Phys. 86, 7041 (1987).
3. G. E. Scuseria and H. F. Schaefer, Chem. Phys. Lett. 152, 382 (1988).
4. J. D. Watts and R. J. Bartlett, J. Chem. Phys. 93, 6104 (1990).

CCSDTQ

Summary

The CC with singles, doubles, triples, and quadruples (CCSDTQ) method approximates the cluster operator as T = T₁ + T₂ + T₃ + T₄. CCSDTQ is a very high-level method and is often capable of providing near-exact energetics for most problems of chemical interest, as long as the number of strongly correlated electrons is not too large (for methods designed to treat genuine strong correlations, see the approximate coupled-pair, or ACP approaches). CCSDTQ has iterative computational costs that scale as n_o⁴n_u⁶, where n_o is the number of correlated occupied orbitals and n_u is the number of correlated unoccupied orbitals.

Sample Code

    from pyscf import gto, scf
    from ccpy.drivers.driver import Driver

    # build molecule using PySCF and run SCF calculation
    mol = gto.M(
        atom=[["O", (0.0, 0.0, -0.0180)],
              ["H", (0.0, 3.030526, -2.117796)],
              ["H", (0.0, -3.030526, -2.117796)]],
        basis="cc-pvdz",
        charge=0,
        spin=0,
        symmetry="C2V",
        cart=False,
        unit="Bohr",
    )
    mf = scf.RHF(mol)
    mf.kernel()
    
    # get the CCpy driver object using PySCF meanfield
    driver = Driver.from_pyscf(mf, nfrozen=1)

    # set calculation parameters
    driver.options["energy_convergence"] = 1.0e-07 # (in hartree)
    driver.options["amp_convergence"] = 1.0e-07
    driver.options["maximum_iterations"] = 80

    # run CCSDTQ calculation
    driver.run_cc(method="ccsdtq")

References

1. N. Oliphant and L. Adamowicz, J. Chem. Phys. 95, 6645 (1991).
2. S. A. Kucharski and R. J. Bartlett, Theor. Chem. Acc. 80, 387 (1991).
3. S. A. Kucharski and R. J. Bartlett, J. Chem. Phys. 97, 4282 (1992).
4. P. Piecuch and L. Adamowicz, J. Chem. Phys. 100, 5792 (1994).

CCSD(T)

Summary

The CCSD(T) method corrects the CCSD energy for the correlation effects due to T₃ clusters using formulas derived using many-body perturbation theory (MBPT). In particular, the CCSD(T) correction includes the leading 4th-order energy correction for T₃ along with 5th-order contribution due to disconnected triples. The inclusion of the latter term distinguishes CCSD(T) from its CCSD[T] precedessor. CCSD(T) has noniterative computational costs that scale as n_o³n₄⁴, where n_o is the number of correlated occupied orbitals and n_u is the number of correlated unoccupied orbitals.

Sample Code

    from pyscf import gto, scf
    from ccpy.drivers.driver import Driver

    # build molecule using PySCF and run SCF calculation
    mol = gto.M(
        atom=[["O", (0.0, 0.0, -0.0180)],
              ["H", (0.0, 3.030526, -2.117796)],
              ["H", (0.0, -3.030526, -2.117796)]],
        basis="cc-pvdz",
        charge=0,
        spin=0,
        symmetry="C2V",
        cart=False,
        unit="Bohr",
    )
    mf = scf.RHF(mol)
    mf.kernel()
    
    # get the CCpy driver object using PySCF meanfield
    driver = Driver.from_pyscf(mf, nfrozen=1)

    # set calculation parameters
    driver.options["energy_convergence"] = 1.0e-07 # (in hartree)
    driver.options["amp_convergence"] = 1.0e-07
    driver.options["maximum_iterations"] = 80

    # run CCSD calculation
    driver.run_cc(method="ccsd")
    # perform CCSD(T) correction
    driver.run_ccp3(method="ccsd(t)")

References

1. K. Raghavachari, G. W. Trucks, J. A. Pople, and M. Head-Gordon, Chem. Phys. Lett. 157, 479 (1989).
2. J. F. Stanton, Chem. Phys. Lett. 281, 130 (1997).
3. S. A. Kucharski and R. J. Bartlett, J. Chem. Phys. 108, 5243 (1998).
4. T. D. Crawford and J. F. Stanton, Int. J. Quantum Chem. 70, 601 (1998).

CR-CC(2,3)

Summary

The CR-CC(2,3) approach is a nonperturbative and noniterative correction to the CCSD energetics that accounts for the correlation effects due to T₃ clusters using formulas derived from the biorthogonal moment energy expansions of CC theory. In particular, CR-CC(2,3) represents the most robust scheme to noniteratively include the effects of connected triples on top of CCSD, and it is capable of providing an accurate description of closed-shell molecules in addition to commonly encountered multireference problems, such as single bond breaking and open-shell radical and diradical species, which are generally beyond the scope of perturbative methods like CCSD(T). The CR-CC(2,3) triples correction uses noniterative steps that scale as n_o³n₄⁴, where n_o is the number of correlated occupied orbitals and n_u is the number of correlated unoccupied orbitals, however, due to the precise form of the expressions defining the CR-CC(2,3) triples correction, it is approximately twice as expensive as its CCSD(T) counterpart. One must also solve the companion left-CCSD system of linear equations (roughly as expensive as CCSD) prior to computing the CR-CC(2,3) correction.

The CR-CC(2,3) calculation returns four distinct energetics, labelled as CR-CC(2,3)_X, for X = A, B, C, and D, where each variant A-D corresponds to a different treatment of the energy denominators entering the formula for the CR-CC(2,3) triples correction. The variant CR-CC(2,3)_A uses the simplest Møller-Plesset form of the energy denominator and is equivalent to the method called CCSD(2)_T. Meanwhile, the CR-CC(2,3)_D result, which employs the full Epstein-Nesbet energy denominator, is generally most accurate and often reported as the CR-CC(2,3) energy (or by its former name, CR-CCSD(T)_L).

Sample Code

    from pyscf import gto, scf
    from ccpy.drivers.driver import Driver

    # build molecule using PySCF and run SCF calculation
    mol = gto.M(
        atom=[["O", (0.0, 0.0, -0.0180)],
              ["H", (0.0, 3.030526, -2.117796)],
              ["H", (0.0, -3.030526, -2.117796)]],
        basis="cc-pvdz",
        charge=0,
        spin=0,
        symmetry="C2V",
        cart=False,
        unit="Bohr",
    )
    mf = scf.RHF(mol)
    mf.kernel()
    
    # get the CCpy driver object using PySCF meanfield
    driver = Driver.from_pyscf(mf, nfrozen=1)

    # set calculation parameters
    driver.options["energy_convergence"] = 1.0e-07 # (in hartree)
    driver.options["amp_convergence"] = 1.0e-07
    driver.options["maximum_iterations"] = 80

    # run CCSD calculation
    driver.run_cc(method="ccsd")
    # build CCSD similarity-transformed Hamiltonian (this overwrites original MO integrals)
    driver.run_hbar(method="ccsd")
    # run companion left-CCSD calculation
    driver.run_leftcc(method="left_ccsd")
    # run CR-CC(2,3) triples correction
    driver.run_ccp3(method="crcc23")

References

1. P. Piecuch and M. Włoch, J. Chem. Phys. 123, 224105 (2005).
2. P. Piecuch, M. Włoch, J. R. Gour, and A. Kinal, Chem. Phys. Lett 418, 467 (2006).
3. M. Włoch, M. D. Lodriguito, P. Piecuch, and J. R. Gour, Mol. Phys. 104, 2149 (2006), 104, 2991 (2006) [Erratum].
4. M. Włoch, J. R. Gour, and P. Piecuch, J. Phys. Chem. A. 111, 11359 (2007).
5. P. Piecuch, J. R. Gour, and M. Włoch, Int. J. Quantum Chem. 108, 2128 (2008).

CR-CC(2,4)

Summary

Sample Code

References

CC3

Summary

Sample Code

    from pyscf import gto, scf
    from ccpy.drivers.driver import Driver

    # build molecule using PySCF and run SCF calculation
    mol = gto.M(
        atom=[["O", (0.0, 0.0, -0.0180)],
              ["H", (0.0, 3.030526, -2.117796)],
              ["H", (0.0, -3.030526, -2.117796)]],
        basis="cc-pvdz",
        charge=0,
        spin=0,
        symmetry="C2V",
        cart=False,
        unit="Bohr",
    )
    mf = scf.RHF(mol)
    mf.kernel()
    
    # get the CCpy driver object using PySCF meanfield
    driver = Driver.from_pyscf(mf, nfrozen=1)

    # set calculation parameters
    driver.options["energy_convergence"] = 1.0e-07 # (in hartree)
    driver.options["amp_convergence"] = 1.0e-07
    driver.options["maximum_iterations"] = 80

    # run CC3 calculation
    driver.run_cc(method="cc3")

References

CCSDt

Summary

The active-orbital-based CCSDt calculation

Sample Code

    from pyscf import gto, scf
    from ccpy.drivers.driver import Driver

    # build molecule using PySCF and run SCF calculation
    mol = gto.M(
        atom=[["F", (0.0, 0.0, -2.66816)],
              ["F", (0.0, 0.0, 2.66816)]],
        basis="cc-pvdz",
        charge=0,
        spin=0,
        symmetry="D2H",
        cart=True,
        unit="Bohr",
    )
    mf = scf.RHF(mol)
    mf.kernel()
    
    # get the CCpy driver object using PySCF meanfield
    driver = Driver.from_pyscf(mf, nfrozen=1)

    # set the active space
    driver.set_active_space(nact_occupied=5, nact_unoccupied=8)

    # set calculation parameters
    driver.options["energy_convergence"] = 1.0e-07 # (in hartree)
    driver.options["amp_convergence"] = 1.0e-07
    driver.options["maximum_iterations"] = 80

    # run CCSDt calculation
    driver.run_cc(method="ccsdt1")

or

    from pyscf import gto, scf
    from ccpy.drivers.driver import Driver
    from ccpy.utilities.pspace import get_active_triples_pspace

    # build molecule using PySCF and run SCF calculation
    mol = gto.M(
        atom=[["F", (0.0, 0.0, -2.66816)],
              ["F", (0.0, 0.0, 2.66816)]],
        basis="cc-pvdz",
        charge=0,
        spin=0,
        symmetry="D2H",
        cart=True,
        unit="Bohr",
    )
    mf = scf.RHF(mol)
    mf.kernel()
    
    # get the CCpy driver object using PySCF meanfield
    driver = Driver.from_pyscf(mf, nfrozen=1)

    # set the active space
    driver.set_active_space(nact_occupied=5, nact_unoccupied=8)
    # get triples entering P space corresponding to the CCSDt truncation scheme
    t3_excitations = get_active_triples_pspace(driver.system,
                                              driver.system.reference_symmetry,
                                              num_active=1)
    # set calculation parameters
    driver.options["energy_convergence"] = 1.0e-07 # (in hartree)
    driver.options["amp_convergence"] = 1.0e-07
    driver.options["maximum_iterations"] = 80

    # Run CC(P) calculation equivalent to CCSDt
    driver.run_ccp(method="ccsdt_p", t3_excitations=t3_excitations)

The latter CC(P)-based approach offers two advantages: (i) it can take advantage of the Abelian point group symmetry of a molecule by restricting the CC calculation to include only those triply excited cluster amplitudes belonging to a particular irrep, as specified by the keyword target_irrep and (ii) it can be used to perform other types of active-orbital-based CCSDt calculations based on restricting num_active occupied/unoccupied indices to the active set. The standard choice of num_active=1 results in the usual CCSDt method, however num_active=2 and num_active=3 result in the CCSDt(II) and CCSDt(III) approaches introduced in Ref. [X].

References

CC(t;3)

Summary

Sample Code

    from pyscf import gto, scf
    from ccpy.drivers.driver import Driver

    # build molecule using PySCF and run SCF calculation
    mol = gto.M(
        atom=[["F", (0.0, 0.0, -2.66816)],
              ["F", (0.0, 0.0, 2.66816)]],
        basis="cc-pvdz",
        charge=0,
        spin=0,
        symmetry="D2H",
        cart=True,
        unit="Bohr",
    )
    mf = scf.RHF(mol)
    mf.kernel()
    
    # get the CCpy driver object using PySCF meanfield
    driver = Driver.from_pyscf(mf, nfrozen=1)

    # set the active space
    driver.set_active_space(nact_occupied=5, nact_unoccupied=8)

    # set calculation parameters
    driver.options["energy_convergence"] = 1.0e-07 # (in hartree)
    driver.options["amp_convergence"] = 1.0e-07
    driver.options["maximum_iterations"] = 80

    # run CCSDt calculation
    driver.run_cc(method="ccsdt1")
    # build CCSD-like similarity-transformed Hamiltonian (this overwrites original MO integrals)
    driver.run_hbar(method="ccsd")
    # run companion left-CCSD-like calculation
    driver.run_leftcc(method="left_ccsd")
    # run CC(t;3) triples correction
    driver.run_ccp3(method="cct3")

or

    from pyscf import gto, scf
    from ccpy.drivers.driver import Driver
    from ccpy.utilities.pspace import get_active_triples_pspace

    # build molecule using PySCF and run SCF calculation
    mol = gto.M(
        atom=[["F", (0.0, 0.0, -2.66816)],
              ["F", (0.0, 0.0, 2.66816)]],
        basis="cc-pvdz",
        charge=0,
        spin=0,
        symmetry="D2H",
        cart=True,
        unit="Bohr",
    )
    mf = scf.RHF(mol)
    mf.kernel()
    
    # get the CCpy driver object using PySCF meanfield
    driver = Driver.from_pyscf(mf, nfrozen=1)

    # set the active space
    driver.set_active_space(nact_occupied=5, nact_unoccupied=8)
    # get triples entering P space corresponding to the CCSDt truncation scheme
    t3_excitations = get_active_triples_pspace(driver.system,
                                              driver.system.reference_symmetry)
    # set calculation parameters
    driver.options["energy_convergence"] = 1.0e-07 # (in hartree)
    driver.options["amp_convergence"] = 1.0e-07    
    driver.options["maximum_iterations"] = 80

    # Run CC(P) calculation equivalent to CCSDt
    driver.run_ccp(method="ccsdt_p", t3_excitations=t3_excitations)
    # build CCSD-like similarity-transformed Hamiltonian (this overwrites original MO integrals)
    driver.run_hbar(method="ccsd")
    # run companion left-CCSD-like calculation
    driver.run_leftcc(method="left_ccsd")
    # run CC(t;3) triples correction
    driver.run_ccp3(method="ccp3", t3_excitations=t3_excitations)

As in the case of the CCSDt calculations, the general CC(P) approach allows one to perform alternative active-orbital-based truncation schemes of the CCSDt(II) and CCSDt(III) types in addition to the standard CCSDt method. The corresponding CC(P;Q) corrections result in the CC(t;3)(II), CC(t;3)(III), and CC(t;3) approaches, respectively.

References

CIPSI-driven CC(P;Q) aimed at converging CCSDT

Summary

Sample Code

from pathlib import Path
import numpy as np
from ccpy.drivers.driver import Driver
from ccpy.utilities.pspace import get_pspace_from_cipsi

TEST_DATA_DIR = str(Path(__file__).parents[1].absolute() / "data")

def test_cipsi_ccpq_h2o():

    driver = Driver.from_gamess(
        logfile=TEST_DATA_DIR + "/h2o/h2o-Re.log",
        onebody=TEST_DATA_DIR + "/h2o/onebody.inp",
        twobody=TEST_DATA_DIR + "/h2o/twobody.inp",
        nfrozen=0,
    )

    civecs = TEST_DATA_DIR + "/h2o/civecs-10k.dat"
    _, t3_excitations, _ = get_pspace_from_cipsi(civecs, driver.system, nexcit=3)

    driver.run_ccp(method="ccsdt_p", t3_excitations=t3_excitations)
    driver.run_hbar(method="ccsdt_p", t3_excitations=t3_excitations)
    driver.run_leftccp(method="left_ccsdt_p", t3_excitations=t3_excitations)
    driver.run_ccp3(method="ccp3", state_index=0, t3_excitations=t3_excitations)

References

1. K. Gururangan, J. E. Deustua, J. Shen, and P. Piecuch, J. Chem. Phys. 155, 174114 (2021)
   (see https://doi.org/10.1063/5.0064400; cf. also https://doi.org/10.48550/arXiv.2107.10994)
   

Adaptive CC(P;Q) aimed at converging CCSDT

Summary

Sample Code

import numpy as np
from pyscf import scf, gto
from ccpy.drivers.driver import Driver
from ccpy.drivers.adaptive import AdaptDriver

def test_adaptive_f2():
    geometry = [["F", (0.0, 0.0, -2.66816)], ["F", (0.0, 0.0, 2.66816)]]
    mol = gto.M(
        atom=geometry,
        basis="cc-pvdz",
        charge=0,
        spin=0,
        symmetry="D2H",
        cart=True,
        unit="Bohr",
    )
    mf = scf.RHF(mol)
    mf.kernel()

    percentages = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
    driver = Driver.from_pyscf(mf, nfrozen=2)
    driver.system.print_info()
    driver.options["RHF_symmetry"] = False
    adaptdriver = AdaptDriver(driver, percentage=percentages)
    adaptdriver.options["energy_tolerance"] = 1.0e-08
    adaptdriver.options["two_body_approx"] = True
    adaptdriver.run()

References

1. K. Gururangan and P. Piecuch, J. Chem. Phys. 159, 084108 (2023)
   (see https://doi.org/10.1063/5.0162873; cf. also https://doi.org/10.48550/arXiv.2306.09638)
   

CC4

Summary

Sample Code

    from pyscf import gto, scf
    from ccpy.drivers.driver import Driver

    # build molecule using PySCF and run SCF calculation
    mol = gto.M(
        atom=[["O", (0.0, 0.0, -0.0180)],
              ["H", (0.0, 3.030526, -2.117796)],
              ["H", (0.0, -3.030526, -2.117796)]],
        basis="cc-pvdz",
        charge=0,
        spin=0,
        symmetry="C2V",
        cart=False,
        unit="Bohr",
    )
    mf = scf.RHF(mol)
    mf.kernel()
    
    # get the CCpy driver object using PySCF meanfield
    driver = Driver.from_pyscf(mf, nfrozen=1)

    # set calculation parameters
    driver.options["energy_convergence"] = 1.0e-07 # (in hartree)
    driver.options["amp_convergence"] = 1.0e-07
    driver.options["maximum_iterations"] = 80

    # run CC4 calculation
    driver.run_cc(method="cc4")

References

Externally Corrected (ec) CC Approaches

CIPSI-driven ec-CC-II and ec-CC-II₃

Summary

Sample Code

from pathlib import Path
import numpy as np
from ccpy.drivers.driver import Driver
from ccpy.utilities.pspace import get_pspace_from_cipsi

TEST_DATA_DIR = str(Path(__file__).parents[1].absolute() / "data")

def test_eccc23_h2o():

    driver = Driver.from_gamess(
        logfile=TEST_DATA_DIR + "/h2o/h2o-Re.log",
        onebody=TEST_DATA_DIR + "/h2o/onebody.inp",
        twobody=TEST_DATA_DIR + "/h2o/twobody.inp",
        nfrozen=0,
    )

    civecs = TEST_DATA_DIR + "/h2o/civecs-10k.dat"
    _, t3_excitations, _ = get_pspace_from_cipsi(civecs, driver.system, nexcit=3)

    driver.run_eccc(method="eccc2", ci_vectors_file=civecs)
    driver.run_hbar(method="ccsd")
    driver.run_leftcc(method="left_ccsd")
    driver.run_ccp3(method="ccp3", state_index=0, t3_excitations=t3_excitations)

References

1. I. Magoulas, K. Gururangan, P. Piecuch, J. E. Deustua, and J. Shen, J. Chem. Theory Comput. 17, 4006 (2021)
   (see https://doi.org/10.1021/acs.jctc.1c00181; cf. also https://doi.org/10.48550/arXiv.2102.10143)

Approximate Coupled-Pair (ACP) Approaches

• ACCD
• ACCSD
• ACCSDt
• ACC(2,3)
• ACC(t;3)

EOMCC approaches for ground, excited, attached, and ionized states

• EOMCCSD
• CR-EOMCC(2,3) and its size-intensive δ-CR-EOMCC(2,3) extension
• EOMCCSD(T)(a)*
• EOM-CC3
• EOMCCSDt
• Excited-state CC(t;3)
• Adaptive CC(P;Q) aimed at converging EOMCCSDT
• EOMCCSDT
• SF-EOMCCSD
• SF-EOMCC(2,3)
• IP-EOMCCSD(2h-1p)
• IP-EOMCCSD(T)(a)*
• Active-space IP-EOMCCSD(3h-2p){N_o} (also known as IP-EOMCCSDt)
• IP-EOMCCSD(3h-2p)
• IP-EOMCCSDT
• EA-EOMCCSD(2p-1h)
• EA-EOMCCSD(T)(a)*
• Active-space EA-EOMCCSD(3p-2h){N_u} (also known as EA-EOMCCSDt)
• EA-EOMCCSD(3p-2h)
• EA-EOMCCSDT
• DEA-EOMCCSD(3p-1h)
• DEA-EOMCCSD(4p-2h)
• DIP-EOMCCSD(3h-1p)
• DIP-EOMCCSD(4h-2p)

Because CCpy is primarily used for CC method development work, we use interfaces to GAMESS and PySCF to obtain the mean-field (typically Hartree-Fock) reference state and associated one- and two-electron integrals in the molecular orbital basis prior to performing the correlated CC calculations. All implementations in CCpy are based on the spin-integrated spinorbital formulation and are compatible with RHF and ROHF references.

Installation and Support

Installation instructions are provided in the CCpy documentation page (https://piecuch-group.github.io/ccpy/). If you have any questions about CCpy or need additional information about its functionality, please e-mail [e-mail].

CCpy Development Team

Karthik Gururangan
Doctoral student, Department of Chemistry, Michigan State University
e-mail: [e-mail]
(lead developer)

Dr. J. Emiliano Deustua
COO and Co-founder, Examol
(co-developer)

Professor Piotr Piecuch
University Distinguished Professor and MSU Research Foundation Professor, Department of Chemistry, Michigan State University
Adjunct Professor, Department of Physics and Astronomy, Michigan State University
e-mail: [e-mail]
(co-developer and principal investigator)

Additional contributors: Tiange Deng (doctoral student, Department of Chemistry, Michigan State University; ACCD and ACCSD options).

Acknowledgements

We acknowledge support from the Chemical Sciences, Geosciences and Biosciences Division, Office of Basic Energy Sciences, Office of Science, U.S. Department of Energy (Grant No. DE-FG02-01ER15228 to Piotr Piecuch).

CCpy is an open-source code under the GPLv3 license developed and maintained by the Piecuch Group at Michigan State University.