# Introduction¶

This chapter explains in general how to use detCI@ORBKIT to process multi-determinantal wave functions to compute expectation values of different one-electron operators of two Configuration Interaction (CI) wavefunctions and :

where are the Slater determinants and are the respective CI coefficients. The Slater determinats itself are built up from the molecular orbitals .

For specific examples, please refer to the ORBKIT examples folder. Here, you can find two tutorials: one for a complex superposition states of and one for a superposition state of . They describe how to reproduce the results shown in

Vincent Pohl, Gunter Hermann, and Jean Christophe Tremblay, “An Open-Source Framework of Analyzing N-electron Dynamics: I. Multi-Determinantal Wave Functions”, arXiv:1701.06885 (2017).

detCI@ORBKIT currently supports the following output file formats:

 QC Program Level of Theory Ground State QC-Data CI-Data Example File PSI4 All CI calculations Molden File Format Output file psi4_fci.in GAMESS-US CIS GAMESS-US Output File Output file Turbomole TD-DFT AOMix File Format “sing_a” file MOLPRO MCSCF Molden File Format Output file molpro_casscf.inp

Note

The underlying ORBKIT modules are called using ORBKIT’s Low-Level Interface.

# How to Read the QC Output¶

After importing the orbkit.read module and the detCI@ORBKIT module, i.e.:

from orbkit import read, detci


we can read the ground state quantum chemistry data, i.e., the molecular geometry and the atomic and molecular orbital data:

qc = read.main_read(fid_molden,itype='molden',all_mo=True)


Here, the quantum chemistry output is parsed into an instance of the QCinfo class (cf. Central Variables). Please note that we have to read the occupied and the virtual molecular orbitals (all_mo=True).

In a similar manner, we can read the Configuration Interaction (CI) output:

qc,ci = detci.ci_read.main_ci_read(qc,fid_psi4,itype='psi4_detci',threshold=0.0)


where threshold specifies a read threshold for the CI coefficients, which can considerably reduce the computional time. The output variable is a list of instances of the CIinfo class, which contains

• ci[a].info: A dictionary with several information on the electronic state such as the state energy, name, multiplicity, etc.
• ci[a].coeffs: An array with CI coefficients
• ci[a].occ: An array with the corresponding occupation patterns

Attention

The function main_ci_read changes its first argument (the QCinfo instance). Within this class the molecular orbitals are reordered according to their symmetry. This is required for all subsequent calculations.

# How to Prepare All Subsequent Calculations¶

The starting point for all grid-based detCI@ORBKIT calculations is the computation of the molecular orbitals and the derivatives thereof ( and ):

from orbkit import grid,core
# Set up the grid
grid.grid_init()
print(grid.get_grid())

# Compute the molecular orbitals and their derviatives
molist = core.rho_compute(qc,
calc_mo=True,
slice_length=1e4,           # Length of grid slice
drv=[None,                  # No derivative
'x','y','z',            # First derviatives
'xx','yy','zz'],        # Second derivatives
numproc=4)                  # Number of subprocesses
molistdrv = molist[1:4]                               # \vec{\nabla} of MOs
molistdrv2 = molist[-3:]                              # \vec{\nabla}^2 of MOs
molist = molist[0]                                    # MOs


Non grid-based calculations, i.e., the electron number, dipole moments in length and velocity gauge, require several expectation values, i.e., , , and

from orbkit.analytical_integrals import get_ao_overlap,get_mo_overlap_matrix
from orbkit.analytical_integrals import get_ao_dipole_matrix
aoom = get_ao_overlap(qc.geo_spec,qc.geo_spec,qc.ao_spec,
ao_spherical=qc.ao_spherical,
drv=[None,                      # No derivative
'x','y','z'])               # First derviatives
dm_aoom = get_ao_dipole_matrix(qc,component=['x','y','z'])

moom = get_mo_overlap_matrix(qc.mo_spec,qc.mo_spec,aoom[0],
numproc=4)               # <m|n>
omr = numpy.zeros((3,) + moom.shape)                  # <m|r|n>
omv = numpy.zeros((3,) + moom.shape)                  # <m|\vec{\nabla}|n>
for i in range(3):
omr[i] = get_mo_overlap_matrix(qc.mo_spec,qc.mo_spec,dm_aoom[i],numproc=4)
omv[i] = get_mo_overlap_matrix(qc.mo_spec,qc.mo_spec,aoom[i+1] ,numproc=4)


# How to Compare CI States¶

Consider two electronic states and . According to the Slater-Condon rules, an expectation value of a one-electron operator

where are the Slater determinants and are the respective CI coefficients, is defined by the following contributions

1. Two identical Slater determinats (zero):

1. Two Slater determinants differing by a single orbital (sing)

1. Two Slater determinants differing by two or more orbitals

Beforehand, these determinants have to brought into maximum coincidence.

All these non-zero contributions can be identified by calling:

zero,sing = detci.occ_check.compare(ci[a],ci[b],numproc=4)


# Electron Density¶

The electron (transition) density is defined by

It can be computed with:

rho_ab = detci.ci_core.rho(zero,sing,molist,slice_length=1e4,numproc=4)


The integral over , which may be evaluated analytically by:

elnum = detci.ci_core.enum(zero,sing,moom)


yields the number of electrons for identical states and zero otherwise.

# Electronic Flux Density and Its Divergence¶

The electronic flux density (or Current Density) is defined as

where symbolizes the spatial derviatives with respect to the electronic coordinates. For the real-valued electronic eigenstates this quantity is purely imaginary. Thus, detCI@ORBKIT exclusively computes the non-vanishing imaginary part of the electronic flux density :

j_ab = detci.ci_core.jab(zero,sing,molist,molistdrv,slice_length=1e4,numproc=4)


Please note that the electronic flux density vanishes for identical states. The divergence of the electronic flux density , which can be computed simply by:

nabla_j_ab = detci.ci_core.jab(zero,sing,molist,molistdrv,slice_length=1e4,numproc=4)


is directly related to the time-derivative of the electron density via the electonic continuity equation

This relation may be reformulated to a convergence test:

Please note that the missing cusp relation for Gaussian basis sets leads to a poor convergence around the nuclei, as shown in arXiv:XXX.XXXXX.

# Electronic Dipole Moment¶

The electronic dipole moment can be defined in length gauge

and in velocity gauge

Both quantities can be directly related to each other

and may be used as another convergence test to directly evaluate the quality of the electronic flux density.

All three quantities can be calculated via:

mu_ab = detci.ci_core.mu(ci[a],ci[b],qc,zero,sing,omr,omv)


Here, mu_ab[0] is , mu_ab[1] corresponds to , and mu_ab[2] is . For identical states, the latter is not defined., and besides, the permanent dipole moment of the molecule, i.e., including the nuclear contributions, is computed in that case.