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Before executing any correlated CC/EOMCC steps, the Hartree-Fock +mean field solution and transformed one- and two-electron integrals must be provided via +an external source. Currently, this can be done in one of three ways: + +1) PySCF + +CCpy is fully interfaced with PySCF and can build the Driver object out of a PySCF mean field. This +approach is arguably the most convenient one since the Hartree-Fock calculation can be run using PySCF +within the same script. :: + + from pyscf import gto, scf + from ccpy import Driver + + # Set up geometry for symmetrically stretched H2O + WATER = [["O", (0.0, 0.0, -0.0180)], + ["H", (0.0, 3.030526, -2.117796)], + ["H", (0.0, -3.030526, -2.117796)]] + # Create PySCF molecule object + mol = gto.M( + atom=WATER, + basis="cc-pvdz", + charge=0, + spin=0, + symmetry="C2V", + cart=True, + unit="Bohr", + ) + # Create PySCF RHF mean field object + mf = scf.RHF(mol) + # Run RHF + mf.kernel() + + # Now set up the CCpy driver object using the PySCF mean field + driver = Driver.from_pyscf(mf, nfrozen=1) + # Print the system information + driver.system.print_info() + +2) GAMESS + +CCpy can read system information and extract transformed one- and two-electron integrals from a completed +GAMESS calculation by passing in the locations of the GAMESS output logfile (``gms_logfile``) and +companion FCIDUMP (``gms_fcidump``). GAMESS can be used to generate FCIDUMP files for RHF and ROHF +calculations using the ``runtyp=fcidump`` option. + +`Important Note: The FCIDUMP option in GAMESS has a bug for high-spin ROHF references. The number of +electrons (NELEC) and number of unpaired electrons (MS2) will have incorrect values upon output. Therefore, +to use FCIDUMP files corresponding to ROHF references, one should manually change the NELEC and MS2 +fields to their proper values.` :: + + from ccpy import Driver + # Set up CCpy driver object using GAMESS logfile (gms_logfile) and FCIDUMP (gms_fcidump) + driver = Driver.from_gamess(gms_logfile, + gms_fcidump, + nfrozen=0) + # Print the system information + driver.system.print_info() + +3) FCIDUMP + +The most general way to pass in information about a mean field is through a single FCIDUMP file. For ROHF +references, it is a good idea to specify the appropriate canonicalization scheme so that the molecular orbital +energies are output correctly (the ROHF single-particle energies are not actually used in correlated computations, +so in practice, this is optional). If no canonicalization is provided, CCpy will default to using ``Guest-Saunders``, +which is most common, but this is not always the correct choice. For example, when using ROHF FCIDUMP files generated +with GAMESS under default settings, the correct canonicalization scheme is ``Roothaan``. + +`Important Note: Currently, CCpy does not use the spatial point group symmetry information contained in the FCIDUMP file. This +will be changed soon.` :: + + from ccpy import Driver + # Set up CCpy driver object using an FCIDUMP file + driver = Driver.from_fcidump(fcidump, nfrozen=0, charge=0, rohf_canonicalization="Roothaan") + # Print the system information + driver.system.print_info() + +CCD +--- +Sample code :: + + from ccpy import Driver + + # Run CCD calculation + driver.run_cc(method="ccd") + +CCSD +---- +Sample code :: + + from ccpy import Driver + + # Run CCSD calculation + driver.run_cc(method="ccsd") + +CCSD(T) +------- +Sample code :: + + from ccpy import Driver + + # Run CCSD calculation + driver.run_cc(method="ccsd") + # Run CCSD(T) triples correction to CCSD energetics + driver.run_ccp3(method="ccsdpt") + +CC3 +--- +Sample code :: + + from ccpy import Driver + + # Run CC3 calculation + driver.run_cc(method="cc3") + +CCSDt +----- +Sample code :: + + from ccpy import Driver, get_active_triples_space + + # Choose the active space for the problem. Here, we are using (2,2). + driver.system.set_active_space(nact_occupied=2, nact_unoccupied=2) + # Obtain the list of triples excitations corresponding to the CCSDt truncation (ground-state symmetry adapted) + t3_excitations = get_active_triples_space(driver.system, target_irrep=driver.system.reference_symmetry) + + # Run active-space CCSDt calculation via general CC(P) solver + driver.run_ccp(method="ccsdt_p", t3_excitations=t3_excitations) + +CCSDT +----- +Sample code :: + + from ccpy import Driver + + # Run CCSDT calculation + driver.run_cc(method="ccsdt") + +Alternatively, full CCSDT calculations are also available by running active-orbital-based CCSDt with full active space. +The advantage of this approach is that it allows for point group symmetry-adapted CCSDT runs. :: + + from ccpy import Driver, get_active_triples_space + + # Choose the active space for the problem. Here, we are using a full active space. + driver.system.set_active_space(nact_occupied=driver.system.noccupied_alpha, nact_unoccupied=driver.system.nunoccupied_beta) + # Obtain the list of triples excitations corresponding to the CCSDt truncation (ground-state symmetry adapted) + t3_excitations = get_active_triples_space(driver.system, target_irrep=driver.system.reference_symmetry) + + # Run full CCSDT calculation via general CC(P) solver + driver.run_ccp(method="ccsdt_p", t3_excitations=t3_excitations) + +CC4 +---- +`Note: CC4 is available for closed-shell references only!` + +Sample code :: + + from ccpy import Driver + + # Run CC4 calculation + driver.run_cc(method="cc4") + +CCSDTQ +------ +`Note: CCSDTQ is available for closed-shell references only!` + +Sample code :: + + from ccpy import Driver + + # Run CCSDTQ calculation + driver.run_cc(method="ccsdtq") + +EOMCCSD +------- +Sample code :: + + # Run the ground-state CCSD calculation + driver.run_cc(method="ccsd") + # Compute and store the CCSD similarity-transformed Hamiltonian (this will overwrite the bare integrals in driver.hamiltonian) + driver.run_hbar(method="ccsd") + # Perform an initial CI-like diagonalization to obtain guess vectors + driver.run_guess(method="cis", multiplicity=1, roots_per_irrep={"A1": 3, "B1": 2, "B2": 2, "A2": 0}) + # Run the EOMCCSD calculation for the specified states. The values `state_index` map one-to-one + # with the guess vectors, so in this example, + # states 1, 2, 3 -> A1 + # states 4, 5 -> B1 + # states 6, 7 -> B2 + driver.run_eomcc(method="eomccsd", state_index=[1, 2, 3, 4, 5, 6, 7]) + +EOM-CC3 +------- +Sample code :: + + # + driver.run_cc(method="cc3") + driver.run_hbar(method="cc3") + driver.run_guess(method="cisd", roots_per_irrep={"A1": 3, "B1": 3, "B2": 2, "A2": 1}, multiplicity=1, nact_occupied=2, nact_unoccupied=4) + driver.run_eomcc(method="eomcc3", state_index=[1, 2, 3, 4, 5, 6, 7, 8, 9]) + +EOMCCSDT(a)* +------------ +Sample code :: + + # Run ground-state CC calculation + driver.run_cc(method="ccsd") + # Obtain the CCSD(T)(a) similarity-transformed Hamiltonian + driver.run_hbar(method="ccsdta") + # Run EOMCCSD-like calculation using CCSD(T)(a) HBar + driver.run_guess(method="cisd", multiplicity=1, roots_per_irrep={"A1": 4, "B1": 2, "B2": 0, "A2": 2}, nact_occupied=3, nact_unoccupied=7) + driver.run_eomcc(method="eomccsd", state_index=[2, 3, 4, 5, 6, 7, 8]) + # Obtain the left eigenstates for each EOMCC root + driver.run_lefteomcc(method="left_ccsd", state_index=[2, 3, 4, 5, 6, 7, 8]) + # Compute EOMCCSDT(a)* excited-state corrections + driver.run_ccp3(method="eomccsdta_star", state_index=[0, 2, 3, 4, 5, 6, 7, 8]) + +EOMCCSDt +-------- + +EOMCCSDT +-------- + +IP-EOMCCSD(2h-1p) +----------------- + +IP-EOMCCSD(3h-2p) +----------------- + +IP-EOMCCSDT(a)* +--------------- + +EA-EOMCCSD(2p-1h) +----------------- + +EA-EOMCCSD(3p-2h) +----------------- + +EA-EOMCCSDT(a)* +--------------- + +DEA-EOMCCSD(2p) +--------------- + +DEA-EOMCCSD(3p-1h) +------------------ + +DEA-EOMCCSD(4p-2h) +------------------ + +SF-EOMCCSD +---------- + +CR-CC(2,3) +---------- + +CR-CC(2,4) +---------- + +CC(t;3) +------- + +CIPSI-driven CC(*P* ;\ *Q*) aimed at converging CCSDT +----------------------------------------------------- + +Adaptive CC(*P* ;\ *Q*) aimed at converging CCSDT +------------------------------------------------- + +CR-EOMCC(2,3) and :math:`\delta`-CR-EOMCC(2,3) +---------------------------------------------- + +ec-CC-II +-------- + +ec-CC-II\ :sub:`3` +------------------ + +ec-CC-II\ :sub:`3,4` +-------------------- diff --git a/_sources/contact_information.rst.txt b/_sources/contact_information.rst.txt new file mode 100644 index 00000000..c14abbaa --- /dev/null +++ b/_sources/contact_information.rst.txt @@ -0,0 +1,18 @@ +CCpy Development Team +##################### + +| Karthik Gururangan (e-mail: gururang@msu.edu) +| Doctoral student, Department of Chemistry, Michigan State University + +| Dr. J. Emiliano Deustua +| COO and Co-founder, Examol + +| Professor Piotr Piecuch +| University Distinguished Professor and Michigan State University Foundation Professor, Department of Chemistry, Michigan State University +| Adjunct Professor, Department of Physics and Astronomy, Michigan State University + +CCpy is an open-source code under the `GPLv3 `_ license. +It is developed and maintained by the `Piecuch Group `_ +at Michigan State University. In addition to using GitHub's Issues feature, feel free to send an e-mail +to gururang@msu.edu if you have any questions about using CCpy or are seeking additional information +about its functionality. diff --git a/_sources/eccc_calculations.rst.txt b/_sources/eccc_calculations.rst.txt new file mode 100644 index 00000000..3f4014b6 --- /dev/null +++ b/_sources/eccc_calculations.rst.txt @@ -0,0 +1,14 @@ +Externally Corrected Coupled-Cluster +#################################### + +Selected-Configuration-Interaction-Based Approaches +*************************************************** + +ec-CC-II +======== + +ec-CC-II\ :sub:`3` +================== + +ec-CC-II\ :sub:`3,4` +==================== diff --git a/_sources/getting_started.rst.txt b/_sources/getting_started.rst.txt new file mode 100644 index 00000000..1d90335a --- /dev/null +++ b/_sources/getting_started.rst.txt @@ -0,0 +1,118 @@ +Getting Started +############### + +Using the PySCF Interface +************************* +Let's build a simple example showing how we can run a ground-state +CCSD calculation for the water molecule described with the cc-pVDZ basis +set using the interface to PySCF. For this example, we will use the +symmetrically stretched geometry from +J. Olsen, P. Jørgensen, H. Koch, A. Balkova, and R. J. Bartlett, *J. Chem. Phys.* **104**, 8007 (1996). + +First, we need to import the relevant modules from PySCF and prepare a RHF +calculation. This is done by first specifying the nuclear +geometry and building the :code:`Molecule` object. Next, an RHF-type :code:`MeanField` +object is constructed, which accepts :code:`Molecule` as an input in order to +execute an RHF calculation. This is all accomplished using the code below :: + + # Import relevant SCF modules from PySCF + from pyscf import scf, gto + # Specify the molecular geometry + geometry = '''O 0.0 0.0 -0.0180 + H 0.0 3.030526 -2.117796 + H 0.0 -3.030526 -2.117796''' + # Construct the PySCF Molecule object described within a specified basis set + mol = gto.M( + atom=geometry, + basis="cc-pvdz", + charge=0, + spin=0, + symmetry="C2V", + cart=False, + unit="Bohr", + ) + # Construct the RHF MeanField object + mf = scf.RHF(mol) + # Run the RHF calculation + mf.kernel() + +In order to run CC calculations on top of this mean-field state using CCpy, +we must import the main calculation driver class, called :code:`Driver`, +using the following command :: + + from ccpy.drivers.driver import Driver + +The :code:`Driver` object can be instantiated using the PySCF mean-field object +as an input along with the number of frozen spatial orbitals, which +should correspond to the chemical core of the molecule. For water, we +will freeze the 1s orbital of the oxygen atom. The :code:`Driver.from_pyscf()` +constructor not processes the PySCF :code:`MeanField` object to obtain and store +important information about the system needed for correlated calculations, +but also performs AO-to-MO integral transformation and sorting steps, storing the +resulting integrals as part of the :code:`Driver` object. +We can execute this driver instantiation step through the PySCF interface as follows :: + + driver = Driver.from_pyscf(mf, nfrozen=1) + driver.system.print_info() + +The above code will create the :code:`Driver` object out of the PySCF :code:`MeanField` +object and print an output of the system information. We are now ready to use the +:code:`Driver` object to run correlated CC calculations. For example, a basic CCSD +calculation is performed using :: + + driver.run_cc(method="ccsd") + +Using the GAMESS Interface +************************** +Let's now run the same example using the interface to GAMESS. This is +slightly more complicated because we need to run GAMESS separately +to obtain the output log file as well as an FCIDUMP file +containing the molecular orbital integrals. To accomplish this, we should +have a working installation of GAMESS and prepare an input script +to run the RHF calculation and produce the associated FCIDUMP. This is +done using the following GAMESS input, called :code:`h2o-ccpvdz.inp` :: + + $contrl scftyp=rhf runtyp=fcidump units=bohr ispher=+1 $end + $system mwords=1 $end + $basis gbasis=ccd $end + $guess guess=huckel $end + $data + H2O molecule / cc-pvdz + cnv 2 + O 8.0 0.000000 0.000000 -0.018000 + H 1.0 0.000000 3.030526 -2.117796 + $end + +Now, we should run this input using whatever configuration of GAMESS +is convenient for your machine, for example, using the :code:`rungms` +script included in the GAMESS package distribution. The following execution +of the GAMESS program :: + + rungms h2o-ccpvdz.inp | tee h2o-ccpvdz.log + +will produce the output file :code:`h2o-ccpvdz.log` as well as the FCIDUMP file +:code:`h2o-ccpvdz.FCIDUMP` located in the same directory. Now, to use the GAMESS +interface of CCpy to begin the CC calculations, we will need to again instantiate +the :code:`Driver` class using mean-field information contained within the GAMESS log +file and FCIDUMP. This can be done using the :code:`Driver.from_gamess()` constructor +as follows :: + + from ccpy.drivers.driver import Driver + + driver = Driver.from_gamess(logfile="h2o-ccpvdz.log", + fcidump="h2o-ccpvdz.FCIDUMP", + nfrozen=1) + driver.system.print_info() + +which again loads the :code:`Driver` object with the information about the RHF solution +computed with GAMESS, freezing the core 1s oxygen orbital for further correlated calculations. +In this case, GAMESS has already executed the AO-to-MO transformation and written the unique +integrals in Mulliken notation within the FCIDUMP file, so this construction simply reads in +the integrals from the FCIDUMP file and sorts and stores them within the :code:`Driver` object +as before. Once we have the :code:`Driver` object instantiated, we can use it to run CC +calculations. Again, a CCSD calculation is performed with :: + + driver.run_cc(method="ccsd") + + + diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 00000000..12b75864 --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,34 @@ +.. ccpy documentation master file, created by + sphinx-quickstart on Tue Oct 17 13:30:35 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +CCpy: A coupled-cluster package written in Python +================================================= +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 +:code:`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. + +.. toctree:: + :maxdepth: 3 + :caption: Contents: + + installation + getting_started + computational_options + walkthrough + references + contact_information + +.. Indices and tables + ================== + * :ref:`genindex` + * :ref:`modindex` + * :ref:`search` +.. diff --git a/_sources/installation.rst.txt b/_sources/installation.rst.txt new file mode 100644 index 00000000..617e0e22 --- /dev/null +++ b/_sources/installation.rst.txt @@ -0,0 +1,62 @@ +Installation +############ +To install CCpy, you will need a Python environment with the following packages: + +* Python 3.8+ +* NumPy (:code:`conda install numpy`) +* gfortran (:code:`conda install -c conda-forge gfortran`) + +Currently, CCpy relies on external software to provide Hartree-Fock molecular orbitals +and the asssociated one- and two-electron integrals as inputs to the CC calculations. +CCpy provides interfaces that process the outputs of mean-field solutions computed using +either GAMESS or PySCF (interfaces to other open-source software, such as Psi4, will be +added in the future). + +In order to use the interface to GAMESS, you must have: + +* A working installation of GAMESS (https://www.msg.chem.iastate.edu/gamess/download.html) +* cclib (:code:`conda install -c conda-forge cclib`) + +In order to use the interface to PySCF, you must have: + +* PySCF (:code:`conda install -c conda-forge pyscf`) + +Once you have these packages installed, you can install CCpy in the same environment using:: + + make all + make install + +The former command compiles the Fortran modules used in CCpy and the latter command +installs a locally editable copy of CCpy using pip (i.e. equivalent to :code:`pip install -e .`). +The location for MKL libraries is taken to be the local version of these libraries installed +within the Conda environment. If Numpy is installed from scratch and configured correctly, it +should come properly loaded with MKL libraries and this will be handled automatically. +If you need to modify this, or any other MKL linking flags, you must edit the Fortran module +Makefile in :code:`/utilities/updates/Makefile`. For a given computer architecture, +the Intel Link Line Advisor is a useful tool to figure out which compiler flags need to be included +(see https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl-link-line-advisor.html#gs.jxf4xw). + +The following is a complete sequence of steps needed to successfully install CCpy compatible with +PySCF using Numpy linked to MKL :: + + conda create --name=ccpy_env python=3.11 + conda activate ccpy_env + + conda install numpy + conda install -c conda-forge gfortran + conda install -c conda-forge cclib + conda install -c conda-forge pyscf + + make all + make install + +It may happen that CCpy fails to execute the DGEMM or other BLAS operations. This +can occur if Numpy is not properly linked to the MKL libraries for its backend. A +common reason for this is if PySCF is installed before Numpy, since PySCF installs +a redundant copy of Numpy that is not linked to optimized BLAS libraries. In order to +fix this, uninstall and reinstall Numpy within the Conda environment and similarly +reinstall PySCF. Before compiling the Fortran modules, it is good to check that the +Numpy backend is linked to MKL libraries using the command :: + + import numpy + numpy.show_config() diff --git a/_sources/references.rst.txt b/_sources/references.rst.txt new file mode 100644 index 00000000..40472feb --- /dev/null +++ b/_sources/references.rst.txt @@ -0,0 +1,3 @@ +References +---------------------------- +[1] J. Olsen, P. Jørgensen, H. Koch, A. Balkova, and R. J. Bartlett, *J. Chem. Phys.* **104**, 8007 (1996). \ No newline at end of file diff --git a/_sources/walkthrough.rst.txt b/_sources/walkthrough.rst.txt new file mode 100644 index 00000000..585482a2 --- /dev/null +++ b/_sources/walkthrough.rst.txt @@ -0,0 +1,137 @@ +Specific Walkthroughs +################### + +Here, we walkthrough some code for performing a few of the standard +CC calculations that are possible using CCpy. In all cases, we will +assume that the :code:`Driver` object has been instantiated, either +manually, or using one of the provided interfaces. This way, the +system is specified within :code:`driver.system` and the molecular +orbital integrals are loaded and available in :code:`driver.hamiltonian`. + +Completely Renormalized (CR) CC Calculations +******************************** +Here is an example of how to perform a CR-CC(2,3) calculation in order to +obtain the nonperturbative correction to the CCSD energetics for the +effects of missing triples using moment energy expansions. :: + + # Run CCSD calculation + driver.run_cc(method="ccsd") + # Obtain CCSD similarity-transformed Hamiltonian + driver.run_hbar(method="ccsd") + # Run left-CCSD calculation + driver.run_leftcc(method="left_ccsd") + # Run CR-CC(2,3) triples correction + driver.run_ccp3(method="crcc23") + +The above commands, which must be run in sequence, first performs the CCSD calculation in order +to obtain the converged :math:`T_{1}` and :math:`T_{2}` clusters and follows this +by computing the one- and two-body components of the CCSD similarity-transformed +Hamiltonan :math:`\bar{H}=(H_N e^{T_1+T_2})_C`. Note that the :code:`run_hbar` +method replaces the bare one- and two-body molecular orbital integrals contained +in :code:`driver.hamiltonian` with their similarity-transformed counterparts. +Next, the :code:`driver.run_leftcc(method="left_ccsd")` method solves the companion left-CCSD system of +linear equations to obtain the :math:`\Lambda_{1}` and :math:`\Lambda_{2}` operators, +which are needed to compute the CR-CC(2,3) triples correction using a single +noniterative step with :code:`driver.run_ccp3(method="crcc23")`. The CR-CC(2,3) +calculation returns four distinct energetics, labelled as CR-CC(2,3)\ :sub:`X`\, for +X = A, B, C, and D, where each variant A-D corresponds to a different treatment of the energy +denominator :math:`E^{(\text{CCSD})} - \langle \Phi_{ijk}^{abc} | \bar{H} | \Phi_{ijk}^{abc} \rangle` +entering the formula for the CR-CC(2,3) triples correction. Note that the variant using +the simplest Moeller-Plesset form of the energy denominator, denoted as CR-CC(2,3)\ :sub:`A`\, +is equivalent to the method called CCSD(2)\ :sub:`T` \ and the +result corresponding to CR-CC(2,3)\ :sub:`D`\, which employs the full Epstein-Nesbet +energy denominator, is generally most accurate and often simply referred to as the +CR-CC(2,3) energy (or by its former name, CR-CCSD(T)\ :sub:`L`\). + +Active-Orbital-Based CC(*P*; *Q*) Calculations +******************************** +Here is an example of how to perform a CC(t;3) calculation in order to +obtain the nonperturbative correction to the CCSDt energetics for the +effects of missing triples using the CC(P;Q) moment expansions. :: + + # Set number of occupied and unoccupied orbitals that are active + nacto = 2 + nactu = 2 + driver.system.set_active_space(nact_occupied=nacto, nact_unoccupied=nactu) + + # Run active-space CCSDt calculation + driver.run_cc(method="ccsdt1") + # Obtain CCSD-like similarity-transformed Hamiltonian + driver.run_hbar(method="ccsd") + # Run left-CCSD calculation + driver.run_leftcc(method="left_ccsd") + # Run CC(t;3) correction + driver.run_ccp3(method="cct3") + +Compared to the above code used to perform CR-CC(2,3), the main difference in CC(t;3) is that the lower-order CCSD +calculation is replaced with the improved active-space CCSDt approach. In order to run +CCSDt, a contiguous set of active orbitals around the Fermi level must be specified using +the :code:`driver.system.set_active_space` method. Here, we are arbitrarily setting up a (2,2) +active space, but the reasonable choice of active orbitals is obviously very dependent on the +specifics of the problem in consideration. This particular example of CC(t;3) relies on what +is known as the two-body approximation, which employs the CR-CC(2,3)-like expressions to formulate +the correction for missing triples to the CCSDt energetics. As a result, the components of the +similarity-transformed Hamiltonian as well as the left-CC deexcitation operator are obtained +with CCSD-like calculations that avoid directly using the of active-orbital-based :math:`t_3` +clusters in the resulting expressions, and instead indirectly incorporates their effects +by using :math:`T_1` and :math:`T_2` clusters provided by CCSDt, which are +relaxed in the presence of :math:`t_3`, and are more accurate than the one- and two-body +components of the cluster operator obtained in CCSD calculations. + +Using the more general CC(P) and CC(P;Q) routines, one can also perform the most complete CC(t;3) +correction that does not invoke the two-body approximation. :: + + # Set number of occupied and unoccupied orbitals that are active + nacto = 2 + nactu = 2 + driver.system.set_active_space(nact_occupied=nacto, nact_unoccupied=nactu) + + # Import and use the helper routine to build the P space corresponding to the choice of active orbitals + from ccpy.utilities.pspace import get_active_space + t3_excitations, pspace = get_active_pspace(driver.system, target_irrep=driver.system.reference_symmetry) + + # Run the active-space CCSDt calculation using the general CC(P) solver + driver.run_ccp(method="ccsdt_p", t3_excitations=t3_excitations) + # Obtain the CCSDt similarity-transformed Hamiltonian + driver.run_hbar(method="ccsdt_p", t3_excitations=t3_excitations) + # Run the left-CCSDt calculation + driver.run_leftccp(method="left_ccsdt_p", t3_excitations=t3_excitations) + # Run the complete CC(t;3) correction + driver.run_ccp3(method="ccp3", state_index=0, t3_excitations=t3_excitations, two_body_approx=False) + +Here, the list of triply excited determinants that enter the P space corresponding to the choice of a (2,2) +orbital active space is constructed with the help of the :code:`get_active_pspace` function, returning +a list of spin-orbital triples excitations stored in the 2D Numpy array called :code:`t3_excitations`. This +list of triply excited determinants entering the P space can also be constrained to include only those belonging +to a specific symmetry irrep, which is specified using the keyword :code:`target_irrep`. +For ground-state calculations of molcules described using an Abelian point group, the target irrep +should correspond to the symmetry of the reference wave function, which is stored in the attribute +:code:`driver.system.reference_symmetry` (for closed-shell systems, this should correspond to the totally +symmetric representation, however, this may not be the case for open shells). Using the P space containing +all singles and doubles ad the list of triples excitations contained in :code:`t3_excitations`, the CC(P) +calculations can be performed using the general :code:`driver.run_ccp` method. This is followed by the running +the :code:`driver.run_hbar` and :code:`driver.run_leftccp` routines, which are used to compute the CCSDt +similarity-transformed Hamiltonian and solve the left-CCSDt system of linear equations, respectively. +Finally, the complete CC(t;3) correction is computed using the :code:`driver.run_ccp3` routine, where it +is important to set the flag :code:`two_body_appox=False` (by default, it is set to :code:`True`). Unlike +the CC(t;3) calculations employing the two-body approximation, which takes advantage of CCSD-like routines +that incorporate the effects of the active-space :math:`t_3` clusters indirectly through relaxation of the +:math:`T_1` and :math:`T_2` components, the full CC(t;3) correction requires constructing the corresponding +CCSDt similarity-transformed Hamiltonian using the :math:`T_1`, :math:`T_2`, and :math:`t_3` operators and +solving for the companion one-, two-, and three-body components of the left-CCSDt deexcitation operator. +As a result, the full CC(t;3) correction provides energetics that are more accurate than its counterpart +employing the two-body approximation, but with computational costs that are larger by roughly a factor of +2-3. For excited-state applications of CC(t;3), the improvement in the energetics often justifies +this increase in computational effort. On the other hand, the two-body approximation is often sufficient for +ground-state CC(t;3) calaculations, with previous applications demonstrating its ability to converge the parent +CCSDT energetics to within a fraction of a millihartree, even in challenging situations featuring stronger +many-electron correlation effects where :math:`T_3` clusters become large and nonperturbative. + +Adaptive CC(*P*; *Q*) Calculations +******************************** + +CIPSI-Driven CC(*P*; *Q*) Calculations +******************************** + +CIPSI-Based Externally Corrected (ec) CC Calculations +******************************** \ No newline at end of file diff --git a/_static/README.md b/_static/README.md new file mode 100644 index 00000000..2f0cf843 --- /dev/null +++ b/_static/README.md @@ -0,0 +1,16 @@ +# Static Doc Directory + +Add any paths that contain custom static files (such as style sheets) here, +relative to the `conf.py` file's directory. +They are copied after the builtin static files, +so a file named "default.css" will overwrite the builtin "default.css". + +The path to this folder is set in the Sphinx `conf.py` file in the line: +```python +templates_path = ['_static'] +``` + +## Examples of file to add to this directory +* Custom Cascading Style Sheets +* Custom JavaScript code +* Static logo images diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 00000000..81415803 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} diff --git a/_static/basic.css b/_static/basic.css new file mode 100644 index 00000000..7ebbd6d0 --- /dev/null +++ b/_static/basic.css @@ -0,0 +1,914 @@ +/* + * Sphinx stylesheet -- basic theme. + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + 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li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + 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+ document.dispatchEvent(event); + }); +}); \ No newline at end of file diff --git a/_static/language_data.js b/_static/language_data.js new file mode 100644 index 00000000..c7fe6c6f --- /dev/null +++ b/_static/language_data.js @@ -0,0 +1,192 @@ +/* + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, if available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename, kind] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +// Global search result kind enum, used by themes to style search results. +class SearchResultKind { + static get index() { return "index"; } + static get object() { return "object"; } + static get text() { return "text"; } + static get title() { return "title"; } +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename, kind] = item; + + let listItem = document.createElement("li"); + // Add a class representing the item's type: + // can be used by a theme's CSS selector for styling + // See SearchResultKind for the class names. + listItem.classList.add(`kind-${kind}`); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = Documentation.ngettext( + "Search finished, found one page matching the search query.", + "Search finished, found ${resultCount} pages matching the search query.", + resultCount, + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename, kind]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlink", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.setAttribute("role", "list"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename, kind]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + const score = Math.round(Scorer.title * queryLower.length / title.length); + const boost = titles[file] === title ? 1 : 0; // add a boost for document titles + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score + boost, + filenames[file], + SearchResultKind.title, + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + SearchResultKind.index, + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + SearchResultKind.object, + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + SearchResultKind.text, + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 00000000..8a96c69a --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/cc_calculations.html b/cc_calculations.html new file mode 100644 index 00000000..19251bbb --- /dev/null +++ b/cc_calculations.html @@ -0,0 +1,181 @@ + + + + + + + + + Coupled-Cluster Options — CCpy documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Coupled-Cluster Options

+
+

Ground-State Calculations

+
+

CCD

+
+
+

CCSD

+
+
+

CCSD(T)

+
+
+

CC3

+
+
+

CCSDt

+
+
+

CCSDT

+
+
+

CCSDTQ

+
+
+
+

Excited-State Calculations

+
+

EOMCCSD

+
+
+

EOM-CC3

+
+
+

EOMCCSDt

+
+
+

EOMCCSDT

+
+
+
+

Electron-Ionizing Calculations

+
+

IP-EOMCCSD(2h-1p)

+
+
+

IP-EOMCCSD(3h-2p)

+
+
+
+

Electron-Attaching Calculations

+
+

EA-EOMCCSD(2p-1h)

+
+
+

EA-EOMCCSD(3p-2h)

+
+
+

DEA-EOMCCSD(2p)

+
+
+

DEA-EOMCCSD(3p-1h)

+
+
+

DEA-EOMCCSD(4p-2h)

+
+
+
+

Spin-Flip Calculations

+
+

SF-EOMCCSD

+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Karthik Gururangan and Piotr Piecuch. Project structure based on the Computational Molecular Science Python Cookiecutter version 1.5.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/ccpq_calculations.html b/ccpq_calculations.html new file mode 100644 index 00000000..85db95da --- /dev/null +++ b/ccpq_calculations.html @@ -0,0 +1,134 @@ + + + + + + + + + CC(P ;Q) Methodologies — CCpy documentation + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

CC(P ;Q) Methodologies

+
+

Ground-State Calculations

+
+

CR-CC(2,3)

+
+
+

CR-CC(2,4)

+
+
+

CC(t;3)

+
+
+

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

+
+
+

Adaptive CC(P ;Q) aimed at converging CCSDT

+
+
+
+

Excited-State Calculations

+
+

CR-EOMCC(2,3) and \(\delta\)-CR-EOMCC(2,3)

+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Karthik Gururangan and Piotr Piecuch. Project structure based on the Computational Molecular Science Python Cookiecutter version 1.5.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/computational_options.html b/computational_options.html new file mode 100644 index 00000000..8ac2657d --- /dev/null +++ b/computational_options.html @@ -0,0 +1,436 @@ + + + + + + + + + Computational Options — CCpy documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Computational Options

+

Here, we provide sample code to get started with running any of the computational options +available in CCpy. Before executing any correlated CC/EOMCC steps, the Hartree-Fock +mean field solution and transformed one- and two-electron integrals must be provided via +an external source. Currently, this can be done in one of three ways:

+
    +

  1. PySCF

    2. +
+

CCpy is fully interfaced with PySCF and can build the Driver object out of a PySCF mean field. This +approach is arguably the most convenient one since the Hartree-Fock calculation can be run using PySCF +within the same script.

+
from pyscf import gto, scf
+from ccpy import Driver
+
+# Set up geometry for symmetrically stretched H2O
+WATER = [["O", (0.0, 0.0, -0.0180)],
+         ["H", (0.0, 3.030526, -2.117796)],
+         ["H", (0.0, -3.030526, -2.117796)]]
+# Create PySCF molecule object
+mol = gto.M(
+    atom=WATER,
+    basis="cc-pvdz",
+    charge=0,
+    spin=0,
+    symmetry="C2V",
+    cart=True,
+    unit="Bohr",
+)
+# Create PySCF RHF mean field object
+mf = scf.RHF(mol)
+# Run RHF
+mf.kernel()
+
+# Now set up the CCpy driver object using the PySCF mean field
+driver = Driver.from_pyscf(mf, nfrozen=1)
+# Print the system information
+driver.system.print_info()
+
+
+
    +

  1. GAMESS

    2. +
+

CCpy can read system information and extract transformed one- and two-electron integrals from a completed +GAMESS calculation by passing in the locations of the GAMESS output logfile (gms_logfile) and +companion FCIDUMP (gms_fcidump). GAMESS can be used to generate FCIDUMP files for RHF and ROHF +calculations using the runtyp=fcidump option.

+

Important Note: The FCIDUMP option in GAMESS has a bug for high-spin ROHF references. The number of +electrons (NELEC) and number of unpaired electrons (MS2) will have incorrect values upon output. Therefore, +to use FCIDUMP files corresponding to ROHF references, one should manually change the NELEC and MS2 +fields to their proper values.

+
from ccpy import Driver
+# Set up CCpy driver object using GAMESS logfile (gms_logfile) and FCIDUMP (gms_fcidump)
+driver = Driver.from_gamess(gms_logfile,
+                            gms_fcidump,
+                            nfrozen=0)
+# Print the system information
+driver.system.print_info()
+
+
+
    +

  1. FCIDUMP

    2. +
+

The most general way to pass in information about a mean field is through a single FCIDUMP file. For ROHF +references, it is a good idea to specify the appropriate canonicalization scheme so that the molecular orbital +energies are output correctly (the ROHF single-particle energies are not actually used in correlated computations, +so in practice, this is optional). If no canonicalization is provided, CCpy will default to using Guest-Saunders, +which is most common, but this is not always the correct choice. For example, when using ROHF FCIDUMP files generated +with GAMESS under default settings, the correct canonicalization scheme is Roothaan.

+

Important Note: Currently, CCpy does not use the spatial point group symmetry information contained in the FCIDUMP file. This +will be changed soon.

+
from ccpy import Driver
+# Set up CCpy driver object using an FCIDUMP file
+driver = Driver.from_fcidump(fcidump, nfrozen=0, charge=0, rohf_canonicalization="Roothaan")
+# Print the system information
+driver.system.print_info()
+
+
+
+

CCD

+

Sample code

+
from ccpy import Driver
+
+# Run CCD calculation
+driver.run_cc(method="ccd")
+
+
+
+
+

CCSD

+

Sample code

+
from ccpy import Driver
+
+# Run CCSD calculation
+driver.run_cc(method="ccsd")
+
+
+
+
+

CCSD(T)

+

Sample code

+
from ccpy import Driver
+
+# Run CCSD calculation
+driver.run_cc(method="ccsd")
+# Run CCSD(T) triples correction to CCSD energetics
+driver.run_ccp3(method="ccsdpt")
+
+
+
+
+

CC3

+

Sample code

+
from ccpy import Driver
+
+# Run CC3 calculation
+driver.run_cc(method="cc3")
+
+
+
+
+

CCSDt

+

Sample code

+
from ccpy import Driver, get_active_triples_space
+
+# Choose the active space for the problem. Here, we are using (2,2).
+driver.system.set_active_space(nact_occupied=2, nact_unoccupied=2)
+# Obtain the list of triples excitations corresponding to the CCSDt truncation (ground-state symmetry adapted)
+t3_excitations = get_active_triples_space(driver.system, target_irrep=driver.system.reference_symmetry)
+
+# Run active-space CCSDt calculation via general CC(P) solver
+driver.run_ccp(method="ccsdt_p", t3_excitations=t3_excitations)
+
+
+
+
+

CCSDT

+

Sample code

+
from ccpy import Driver
+
+# Run CCSDT calculation
+driver.run_cc(method="ccsdt")
+
+
+

Alternatively, full CCSDT calculations are also available by running active-orbital-based CCSDt with full active space. +The advantage of this approach is that it allows for point group symmetry-adapted CCSDT runs.

+
from ccpy import Driver, get_active_triples_space
+
+# Choose the active space for the problem. Here, we are using a full active space.
+driver.system.set_active_space(nact_occupied=driver.system.noccupied_alpha, nact_unoccupied=driver.system.nunoccupied_beta)
+# Obtain the list of triples excitations corresponding to the CCSDt truncation (ground-state symmetry adapted)
+t3_excitations = get_active_triples_space(driver.system, target_irrep=driver.system.reference_symmetry)
+
+# Run full CCSDT calculation via general CC(P) solver
+driver.run_ccp(method="ccsdt_p", t3_excitations=t3_excitations)
+
+
+
+
+

CC4

+

Note: CC4 is available for closed-shell references only!

+

Sample code

+
from ccpy import Driver
+
+# Run CC4 calculation
+driver.run_cc(method="cc4")
+
+
+
+
+

CCSDTQ

+

Note: CCSDTQ is available for closed-shell references only!

+

Sample code

+
from ccpy import Driver
+
+# Run CCSDTQ calculation
+driver.run_cc(method="ccsdtq")
+
+
+
+
+

EOMCCSD

+

Sample code

+
# Run the ground-state CCSD calculation
+driver.run_cc(method="ccsd")
+# Compute and store the CCSD similarity-transformed Hamiltonian (this will overwrite the bare integrals in driver.hamiltonian)
+driver.run_hbar(method="ccsd")
+# Perform an initial CI-like diagonalization to obtain guess vectors
+driver.run_guess(method="cis", multiplicity=1, roots_per_irrep={"A1": 3, "B1": 2, "B2": 2, "A2": 0})
+# Run the EOMCCSD calculation for the specified states. The values `state_index` map one-to-one
+# with the guess vectors, so in this example,
+# states 1, 2, 3 -> A1
+# states 4, 5 -> B1
+# states 6, 7 -> B2
+driver.run_eomcc(method="eomccsd", state_index=[1, 2, 3, 4, 5, 6, 7])
+
+
+
+
+

EOM-CC3

+

Sample code

+
#
+driver.run_cc(method="cc3")
+driver.run_hbar(method="cc3")
+driver.run_guess(method="cisd", roots_per_irrep={"A1": 3, "B1": 3, "B2": 2, "A2": 1}, multiplicity=1, nact_occupied=2, nact_unoccupied=4)
+driver.run_eomcc(method="eomcc3", state_index=[1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+
+
+
+

EOMCCSDT(a)*

+

Sample code

+
# Run ground-state CC calculation
+driver.run_cc(method="ccsd")
+# Obtain the CCSD(T)(a) similarity-transformed Hamiltonian
+driver.run_hbar(method="ccsdta")
+# Run EOMCCSD-like calculation using CCSD(T)(a) HBar
+driver.run_guess(method="cisd", multiplicity=1, roots_per_irrep={"A1": 4, "B1": 2, "B2": 0, "A2": 2},  nact_occupied=3, nact_unoccupied=7)
+driver.run_eomcc(method="eomccsd", state_index=[2, 3, 4, 5, 6, 7, 8])
+# Obtain the left eigenstates for each EOMCC root
+driver.run_lefteomcc(method="left_ccsd", state_index=[2, 3, 4, 5, 6, 7, 8])
+# Compute EOMCCSDT(a)* excited-state corrections
+driver.run_ccp3(method="eomccsdta_star", state_index=[0, 2, 3, 4, 5, 6, 7, 8])
+
+
+
+
+

EOMCCSDt

+
+
+

EOMCCSDT

+
+
+

IP-EOMCCSD(2h-1p)

+
+
+

IP-EOMCCSD(3h-2p)

+
+
+

IP-EOMCCSDT(a)*

+
+
+

EA-EOMCCSD(2p-1h)

+
+
+

EA-EOMCCSD(3p-2h)

+
+
+

EA-EOMCCSDT(a)*

+
+
+

DEA-EOMCCSD(2p)

+
+
+

DEA-EOMCCSD(3p-1h)

+
+
+

DEA-EOMCCSD(4p-2h)

+
+
+

SF-EOMCCSD

+
+
+

CR-CC(2,3)

+
+
+

CR-CC(2,4)

+
+
+

CC(t;3)

+
+
+

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

+
+
+

Adaptive CC(P ;Q) aimed at converging CCSDT

+
+
+

CR-EOMCC(2,3) and \(\delta\)-CR-EOMCC(2,3)

+
+
+

ec-CC-II

+
+
+

ec-CC-II3

+
+
+

ec-CC-II3,4

+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Karthik Gururangan and Piotr Piecuch. Project structure based on the Computational Molecular Science Python Cookiecutter version 1.5.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/contact_information.html b/contact_information.html new file mode 100644 index 00000000..f3865ed4 --- /dev/null +++ b/contact_information.html @@ -0,0 +1,130 @@ + + + + + + + + + CCpy Development Team — CCpy documentation + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

CCpy Development Team

+
+
Karthik Gururangan (e-mail: gururang@msu.edu)
+
Doctoral student, Department of Chemistry, Michigan State University
+
+
+
Dr. J. Emiliano Deustua
+
COO and Co-founder, Examol
+
+
+
Professor Piotr Piecuch
+
University Distinguished Professor and Michigan State University Foundation Professor, Department of Chemistry, Michigan State University
+
Adjunct Professor, Department of Physics and Astronomy, Michigan State University
+
+

CCpy is an open-source code under the GPLv3 license. +It is developed and maintained by the Piecuch Group +at Michigan State University. In addition to using GitHub’s Issues feature, feel free to send an e-mail +to gururang@msu.edu if you have any questions about using CCpy or are seeking additional information +about its functionality.

+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Karthik Gururangan and Piotr Piecuch. Project structure based on the Computational Molecular Science Python Cookiecutter version 1.5.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/eccc_calculations.html b/eccc_calculations.html new file mode 100644 index 00000000..ddabf792 --- /dev/null +++ b/eccc_calculations.html @@ -0,0 +1,121 @@ + + + + + + + + + Externally Corrected Coupled-Cluster — CCpy documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Externally Corrected Coupled-Cluster

+
+

Selected-Configuration-Interaction-Based Approaches

+
+

ec-CC-II

+
+
+

ec-CC-II3

+
+
+

ec-CC-II3,4

+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Karthik Gururangan and Piotr Piecuch. Project structure based on the Computational Molecular Science Python Cookiecutter version 1.5.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/genindex.html b/genindex.html new file mode 100644 index 00000000..a327bea4 --- /dev/null +++ b/genindex.html @@ -0,0 +1,110 @@ + + + + + + + + Index — CCpy documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Index
    • +
  • +
    • +
+
+
+
+
+ + +

Index

+ +
+ +
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Karthik Gururangan and Piotr Piecuch. Project structure based on the Computational Molecular Science Python Cookiecutter version 1.5.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/getting_started.html b/getting_started.html new file mode 100644 index 00000000..a1b26287 --- /dev/null +++ b/getting_started.html @@ -0,0 +1,232 @@ + + + + + + + + + Getting Started — CCpy documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Getting Started

+
+

Using the PySCF Interface

+

Let’s build a simple example showing how we can run a ground-state +CCSD calculation for the water molecule described with the cc-pVDZ basis +set using the interface to PySCF. For this example, we will use the +symmetrically stretched geometry from +J. Olsen, P. Jørgensen, H. Koch, A. Balkova, and R. J. Bartlett, J. Chem. Phys. 104, 8007 (1996).

+

First, we need to import the relevant modules from PySCF and prepare a RHF +calculation. This is done by first specifying the nuclear +geometry and building the Molecule object. Next, an RHF-type MeanField +object is constructed, which accepts Molecule as an input in order to +execute an RHF calculation. This is all accomplished using the code below

+
# Import relevant SCF modules from PySCF
+from pyscf import scf, gto
+# Specify the molecular geometry
+geometry = '''O  0.0   0.0       -0.0180
+              H  0.0   3.030526  -2.117796
+              H  0.0  -3.030526  -2.117796'''
+# Construct the PySCF Molecule object described within a specified basis set
+mol = gto.M(
+        atom=geometry,
+        basis="cc-pvdz",
+        charge=0,
+        spin=0,
+        symmetry="C2V",
+        cart=False,
+        unit="Bohr",
+)
+# Construct the RHF MeanField object
+mf = scf.RHF(mol)
+# Run the RHF calculation
+mf.kernel()
+
+
+

In order to run CC calculations on top of this mean-field state using CCpy, +we must import the main calculation driver class, called Driver, +using the following command

+
from ccpy.drivers.driver import Driver
+
+
+

The Driver object can be instantiated using the PySCF mean-field object +as an input along with the number of frozen spatial orbitals, which +should correspond to the chemical core of the molecule. For water, we +will freeze the 1s orbital of the oxygen atom. The Driver.from_pyscf() +constructor not processes the PySCF MeanField object to obtain and store +important information about the system needed for correlated calculations, +but also performs AO-to-MO integral transformation and sorting steps, storing the +resulting integrals as part of the Driver object. +We can execute this driver instantiation step through the PySCF interface as follows

+
driver = Driver.from_pyscf(mf, nfrozen=1)
+driver.system.print_info()
+
+
+

The above code will create the Driver object out of the PySCF MeanField +object and print an output of the system information. We are now ready to use the +Driver object to run correlated CC calculations. For example, a basic CCSD +calculation is performed using

+
driver.run_cc(method="ccsd")
+
+
+
+
+

Using the GAMESS Interface

+

Let’s now run the same example using the interface to GAMESS. This is +slightly more complicated because we need to run GAMESS separately +to obtain the output log file as well as an FCIDUMP file +containing the molecular orbital integrals. To accomplish this, we should +have a working installation of GAMESS and prepare an input script +to run the RHF calculation and produce the associated FCIDUMP. This is +done using the following GAMESS input, called h2o-ccpvdz.inp

+
 $contrl scftyp=rhf runtyp=fcidump units=bohr ispher=+1 $end
+ $system mwords=1 $end
+ $basis gbasis=ccd $end
+ $guess guess=huckel $end
+ $data
+H2O molecule / cc-pvdz
+cnv 2
+O  8.0   0.000000   0.000000   -0.018000
+H  1.0   0.000000   3.030526   -2.117796
+ $end
+
+
+

Now, we should run this input using whatever configuration of GAMESS +is convenient for your machine, for example, using the rungms +script included in the GAMESS package distribution. The following execution +of the GAMESS program

+
rungms h2o-ccpvdz.inp | tee h2o-ccpvdz.log
+
+
+

will produce the output file h2o-ccpvdz.log as well as the FCIDUMP file +h2o-ccpvdz.FCIDUMP located in the same directory. Now, to use the GAMESS +interface of CCpy to begin the CC calculations, we will need to again instantiate +the Driver class using mean-field information contained within the GAMESS log +file and FCIDUMP. This can be done using the Driver.from_gamess() constructor +as follows

+
from ccpy.drivers.driver import Driver
+
+driver = Driver.from_gamess(logfile="h2o-ccpvdz.log",
+                            fcidump="h2o-ccpvdz.FCIDUMP",
+                            nfrozen=1)
+driver.system.print_info()
+
+
+

which again loads the Driver object with the information about the RHF solution +computed with GAMESS, freezing the core 1s oxygen orbital for further correlated calculations. +In this case, GAMESS has already executed the AO-to-MO transformation and written the unique +integrals in Mulliken notation within the FCIDUMP file, so this construction simply reads in +the integrals from the FCIDUMP file and sorts and stores them within the Driver object +as before. Once we have the Driver object instantiated, we can use it to run CC +calculations. Again, a CCSD calculation is performed with

+
driver.run_cc(method="ccsd")
+
+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Karthik Gururangan and Piotr Piecuch. Project structure based on the Computational Molecular Science Python Cookiecutter version 1.5.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 00000000..8e86b176 --- /dev/null +++ b/index.html @@ -0,0 +1,178 @@ + + + + + + + + + CCpy: A coupled-cluster package written in Python — CCpy documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

CCpy: A coupled-cluster package written in Python

+

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.

+
+

Contents:

+ +
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Karthik Gururangan and Piotr Piecuch. Project structure based on the Computational Molecular Science Python Cookiecutter version 1.5.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/installation.html b/installation.html new file mode 100644 index 00000000..dce07a62 --- /dev/null +++ b/installation.html @@ -0,0 +1,173 @@ + + + + + + + + + Installation — CCpy documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Installation

+

To install CCpy, you will need a Python environment with the following packages:

+
    +

  • Python 3.8+

    • +

  • NumPy (conda install numpy)

    • +

  • gfortran (conda install -c conda-forge gfortran)

    • +
+

Currently, CCpy relies on external software to provide Hartree-Fock molecular orbitals +and the asssociated one- and two-electron integrals as inputs to the CC calculations. +CCpy provides interfaces that process the outputs of mean-field solutions computed using +either GAMESS or PySCF (interfaces to other open-source software, such as Psi4, will be +added in the future).

+

In order to use the interface to GAMESS, you must have:

+ +

In order to use the interface to PySCF, you must have:

+
    +

  • PySCF (conda install -c conda-forge pyscf)

    • +
+

Once you have these packages installed, you can install CCpy in the same environment using:

+
make all
+make install
+
+
+

The former command compiles the Fortran modules used in CCpy and the latter command +installs a locally editable copy of CCpy using pip (i.e. equivalent to pip install -e .). +The location for MKL libraries is taken to be the local version of these libraries installed +within the Conda environment. If Numpy is installed from scratch and configured correctly, it +should come properly loaded with MKL libraries and this will be handled automatically. +If you need to modify this, or any other MKL linking flags, you must edit the Fortran module +Makefile in /utilities/updates/Makefile. For a given computer architecture, +the Intel Link Line Advisor is a useful tool to figure out which compiler flags need to be included +(see https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl-link-line-advisor.html#gs.jxf4xw).

+

The following is a complete sequence of steps needed to successfully install CCpy compatible with +PySCF using Numpy linked to MKL

+
conda create --name=ccpy_env python=3.11
+conda activate ccpy_env
+
+conda install numpy
+conda install -c conda-forge gfortran
+conda install -c conda-forge cclib
+conda install -c conda-forge pyscf
+
+make all
+make install
+
+
+

It may happen that CCpy fails to execute the DGEMM or other BLAS operations. This +can occur if Numpy is not properly linked to the MKL libraries for its backend. A +common reason for this is if PySCF is installed before Numpy, since PySCF installs +a redundant copy of Numpy that is not linked to optimized BLAS libraries. In order to +fix this, uninstall and reinstall Numpy within the Conda environment and similarly +reinstall PySCF. Before compiling the Fortran modules, it is good to check that the +Numpy backend is linked to MKL libraries using the command

+
import numpy
+numpy.show_config()
+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Karthik Gururangan and Piotr Piecuch. Project structure based on the Computational Molecular Science Python Cookiecutter version 1.5.

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+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
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+ + + + \ No newline at end of file diff --git a/objects.inv b/objects.inv new file mode 100644 index 00000000..7b761695 Binary files /dev/null and b/objects.inv differ diff --git a/references.html b/references.html new file mode 100644 index 00000000..0a3c3a4a --- /dev/null +++ b/references.html @@ -0,0 +1,115 @@ + + + + + + + + + References — CCpy documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

References

+

[1] J. Olsen, P. Jørgensen, H. Koch, A. Balkova, and R. J. Bartlett, J. Chem. Phys. 104, 8007 (1996).

+
+ + +
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© Copyright 2023, Karthik Gururangan and Piotr Piecuch. Project structure based on the Computational Molecular Science Python Cookiecutter version 1.5.

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© Copyright 2023, Karthik Gururangan and Piotr Piecuch. Project structure based on the Computational Molecular Science Python Cookiecutter version 1.5.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
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+ + + + + + + + + \ No newline at end of file diff --git a/searchindex.js b/searchindex.js new file mode 100644 index 00000000..d6fa772d --- /dev/null +++ b/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"alltitles": {"Active-Orbital-Based CC(P; Q) Calculations": [[9, "active-orbital-based-cc-p-q-calculations"]], "Adaptive CC(P ;Q) aimed at converging CCSDT": [[1, "adaptive-cc-p-q-aimed-at-converging-ccsdt"], [2, "adaptive-cc-p-q-aimed-at-converging-ccsdt"]], "Adaptive CC(P; Q) Calculations": [[9, "adaptive-cc-p-q-calculations"]], "CC(P ;Q) Methodologies": [[1, null]], "CC(t;3)": [[1, "cc-t-3"], [2, "cc-t-3"]], "CC3": [[0, "cc3"], [2, "cc3"]], "CC4": [[2, "cc4"]], "CCD": [[0, "ccd"], [2, "ccd"]], "CCSD": [[0, "ccsd"], [2, "ccsd"]], "CCSD(T)": [[0, "ccsd-t"], [2, "ccsd-t"]], "CCSDT": [[0, "id1"], [2, "id1"]], "CCSDTQ": [[0, "ccsdtq"], [2, "ccsdtq"]], "CCSDt": [[0, "ccsdt"], [2, "ccsdt"]], "CCpy Development Team": [[3, null]], "CCpy: A coupled-cluster package written in Python": [[6, 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"written": 6}}) \ No newline at end of file diff --git a/walkthrough.html b/walkthrough.html new file mode 100644 index 00000000..cd9e2b9b --- /dev/null +++ b/walkthrough.html @@ -0,0 +1,255 @@ + + + + + + + + + Specific Walkthroughs — CCpy documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Specific Walkthroughs

+

Here, we walkthrough some code for performing a few of the standard +CC calculations that are possible using CCpy. In all cases, we will +assume that the Driver object has been instantiated, either +manually, or using one of the provided interfaces. This way, the +system is specified within driver.system and the molecular +orbital integrals are loaded and available in driver.hamiltonian.

+
+

Completely Renormalized (CR) CC Calculations

+

Here is an example of how to perform a CR-CC(2,3) calculation in order to +obtain the nonperturbative correction to the CCSD energetics for the +effects of missing triples using moment energy expansions.

+
# Run CCSD calculation
+driver.run_cc(method="ccsd")
+# Obtain CCSD similarity-transformed Hamiltonian
+driver.run_hbar(method="ccsd")
+# Run left-CCSD calculation
+driver.run_leftcc(method="left_ccsd")
+# Run CR-CC(2,3) triples correction
+driver.run_ccp3(method="crcc23")
+
+
+

The above commands, which must be run in sequence, first performs the CCSD calculation in order +to obtain the converged \(T_{1}\) and \(T_{2}\) clusters and follows this +by computing the one- and two-body components of the CCSD similarity-transformed +Hamiltonan \(\bar{H}=(H_N e^{T_1+T_2})_C\). Note that the run_hbar +method replaces the bare one- and two-body molecular orbital integrals contained +in driver.hamiltonian with their similarity-transformed counterparts. +Next, the driver.run_leftcc(method="left_ccsd") method solves the companion left-CCSD system of +linear equations to obtain the \(\Lambda_{1}\) and \(\Lambda_{2}\) operators, +which are needed to compute the CR-CC(2,3) triples correction using a single +noniterative step with driver.run_ccp3(method="crcc23"). 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 +denominator \(E^{(\text{CCSD})} - \langle \Phi_{ijk}^{abc} | \bar{H} | \Phi_{ijk}^{abc} \rangle\) +entering the formula for the CR-CC(2,3) triples correction. Note that the variant using +the simplest Moeller-Plesset form of the energy denominator, denoted as CR-CC(2,3)A, +is equivalent to the method called CCSD(2)T and the +result corresponding to CR-CC(2,3)D, which employs the full Epstein-Nesbet +energy denominator, is generally most accurate and often simply referred to as the +CR-CC(2,3) energy (or by its former name, CR-CCSD(T)L).

+
+
+

Active-Orbital-Based CC(P; Q) Calculations

+

Here is an example of how to perform a CC(t;3) calculation in order to +obtain the nonperturbative correction to the CCSDt energetics for the +effects of missing triples using the CC(P;Q) moment expansions.

+
# Set number of occupied and unoccupied orbitals that are active
+nacto = 2
+nactu = 2
+driver.system.set_active_space(nact_occupied=nacto, nact_unoccupied=nactu)
+
+# Run active-space CCSDt calculation
+driver.run_cc(method="ccsdt1")
+# Obtain CCSD-like similarity-transformed Hamiltonian
+driver.run_hbar(method="ccsd")
+# Run left-CCSD calculation
+driver.run_leftcc(method="left_ccsd")
+# Run CC(t;3) correction
+driver.run_ccp3(method="cct3")
+
+
+

Compared to the above code used to perform CR-CC(2,3), the main difference in CC(t;3) is that the lower-order CCSD +calculation is replaced with the improved active-space CCSDt approach. In order to run +CCSDt, a contiguous set of active orbitals around the Fermi level must be specified using +the driver.system.set_active_space method. Here, we are arbitrarily setting up a (2,2) +active space, but the reasonable choice of active orbitals is obviously very dependent on the +specifics of the problem in consideration. This particular example of CC(t;3) relies on what +is known as the two-body approximation, which employs the CR-CC(2,3)-like expressions to formulate +the correction for missing triples to the CCSDt energetics. As a result, the components of the +similarity-transformed Hamiltonian as well as the left-CC deexcitation operator are obtained +with CCSD-like calculations that avoid directly using the of active-orbital-based \(t_3\) +clusters in the resulting expressions, and instead indirectly incorporates their effects +by using \(T_1\) and \(T_2\) clusters provided by CCSDt, which are +relaxed in the presence of \(t_3\), and are more accurate than the one- and two-body +components of the cluster operator obtained in CCSD calculations.

+

Using the more general CC(P) and CC(P;Q) routines, one can also perform the most complete CC(t;3) +correction that does not invoke the two-body approximation.

+
# Set number of occupied and unoccupied orbitals that are active
+nacto = 2
+nactu = 2
+driver.system.set_active_space(nact_occupied=nacto, nact_unoccupied=nactu)
+
+# Import and use the helper routine to build the P space corresponding to the choice of active orbitals
+from ccpy.utilities.pspace import get_active_space
+t3_excitations, pspace = get_active_pspace(driver.system, target_irrep=driver.system.reference_symmetry)
+
+# Run the active-space CCSDt calculation using the general CC(P) solver
+driver.run_ccp(method="ccsdt_p", t3_excitations=t3_excitations)
+# Obtain the CCSDt similarity-transformed Hamiltonian
+driver.run_hbar(method="ccsdt_p", t3_excitations=t3_excitations)
+# Run the left-CCSDt calculation
+driver.run_leftccp(method="left_ccsdt_p", t3_excitations=t3_excitations)
+# Run the complete CC(t;3) correction
+driver.run_ccp3(method="ccp3", state_index=0, t3_excitations=t3_excitations, two_body_approx=False)
+
+
+

Here, the list of triply excited determinants that enter the P space corresponding to the choice of a (2,2) +orbital active space is constructed with the help of the get_active_pspace function, returning +a list of spin-orbital triples excitations stored in the 2D Numpy array called t3_excitations. This +list of triply excited determinants entering the P space can also be constrained to include only those belonging +to a specific symmetry irrep, which is specified using the keyword target_irrep. +For ground-state calculations of molcules described using an Abelian point group, the target irrep +should correspond to the symmetry of the reference wave function, which is stored in the attribute +driver.system.reference_symmetry (for closed-shell systems, this should correspond to the totally +symmetric representation, however, this may not be the case for open shells). Using the P space containing +all singles and doubles ad the list of triples excitations contained in t3_excitations, the CC(P) +calculations can be performed using the general driver.run_ccp method. This is followed by the running +the driver.run_hbar and driver.run_leftccp routines, which are used to compute the CCSDt +similarity-transformed Hamiltonian and solve the left-CCSDt system of linear equations, respectively. +Finally, the complete CC(t;3) correction is computed using the driver.run_ccp3 routine, where it +is important to set the flag two_body_appox=False (by default, it is set to True). Unlike +the CC(t;3) calculations employing the two-body approximation, which takes advantage of CCSD-like routines +that incorporate the effects of the active-space \(t_3\) clusters indirectly through relaxation of the +\(T_1\) and \(T_2\) components, the full CC(t;3) correction requires constructing the corresponding +CCSDt similarity-transformed Hamiltonian using the \(T_1\), \(T_2\), and \(t_3\) operators and +solving for the companion one-, two-, and three-body components of the left-CCSDt deexcitation operator. +As a result, the full CC(t;3) correction provides energetics that are more accurate than its counterpart +employing the two-body approximation, but with computational costs that are larger by roughly a factor of +2-3. For excited-state applications of CC(t;3), the improvement in the energetics often justifies +this increase in computational effort. On the other hand, the two-body approximation is often sufficient for +ground-state CC(t;3) calaculations, with previous applications demonstrating its ability to converge the parent +CCSDT energetics to within a fraction of a millihartree, even in challenging situations featuring stronger +many-electron correlation effects where \(T_3\) clusters become large and nonperturbative.

+
+
+

Adaptive CC(P; Q) Calculations

+
+
+

CIPSI-Driven CC(P; Q) Calculations

+
+
+

CIPSI-Based Externally Corrected (ec) CC Calculations

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+

© Copyright 2023, Karthik Gururangan and Piotr Piecuch. Project structure based on the Computational Molecular Science Python Cookiecutter version 1.5.

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+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
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