Python Projects: Configuration Interaction Singles (CIS)

Overview

In this project, you will learn how to write a restricted configuration interaction with single excitations (CIS) code using the Python interface to the Psi4 electronic structure package. Most of Psi4's core libraries (integral generation, SCF solver, etc.) are exposed to Python, which facilitates the development of short, easy to understand codes for complex electronic structure methods. For example, you will soon find that it is possible to write a working CIS code in Python in only a few dozen lines!

Background:

Note: we use atomic units throughout this tutorial.

At the Hartree-Fock level of theory, the

*N*-electron wave function is approximated as an antisymmetrized product of

*N*one-electron functions called molecular orbitals (MOs) (a Slater determinant). The simplest representation of an excited-state wave function would be a single Slater determinant comprised of a different set of molecular orbitals, but we can obtain a slightly better description of the excited state by expressing it as a linear combination of Slater determinants that differ by the ground-state configuration by one electron. What we are describing here is a configuration interaction with single excitations (CIS) wave function; the CIS wave function for the

*n*

^{th}excited state can be expressed as

Here, the symbol

*φ*represents a Slater determinant that is singly excited relative to the reference determinant, and the indices

_{i}^{a}*i*and

*a*represent a spin orbitals that are occupied and unoccupied (virtual) in the reference, respectively.

The expansion coefficients and energy of the excited state can be determined by testing the Schrödinger equation with one of the singly-excited configurations to obtain:

We can form similar expressions for all excited states, and, taken together, these expressions become an eigenvalue equation

where

**H**is the matrix representation of the Hamiltonian in the basis of singly-excited determinants. The elements of this matrix are given by

where

*F*represents the Fock matrix, and ⟨

*aj*||

*ib*⟩ represents an antisymmetrized electron-repulsion integral in physicists' notation. Recall that the Fock matrix for restricted Hartree-Fock theory is diagonal in the MO basis, so this expression simplifies sligthly:

where ε

_{i}and ε

_{a}represent orbital energies.

It turns out that when the eigenvalues of the CIS Hamiltonian represented in this spin-orbital basis correspond to energies for CIS states of two different spin symmetries: singlets and triplets. If we are only interested in the singlet states, then we can

**spin adapt**our basis functions and represent the Hamiltonian matrix in this new spin-adapted basis. The singlet spin-adapted basis functions are given by

and the corresponding matrix elements of the Hamiltonian are

Using chemists' notation, these matrix elements are

Note that the orbital labels

*i, j, a,*and

*b*no longer represent spin orbitals; they represent spatial orbitals. The benefits of spin-adaptation here are two-fold. First, the dimension of the Hamiltonian is reduced by a factor of two. Second, because matrix diagonalization scales as the third power of matrix size, the cost of the diagonalization procedure is reduced by a factor of eight!

Procedure:

Step 1. Singlet excitation energies

Working with Psi4 through Python is quite simple. Start by opening a file (e.g. cis.py) using your favorite text editor. The first thing we should do is tell Python where the appropriate Psi4 libraries reside. If you installed Psi4 yourself, the directory you want is the "lib" directory inside your install directory. If you are writing this code on FSU's RCC computers, then you can use the version I installed:

import sys sys.path.insert(0, '/panfs/storage.local/genacc/home/adeprince/software/psi4-1.0-04_11_17/install/lib/') import psi4

Next, you should import numpy libraries, as they contain some really useful features for linear algebra and tensory manipulation:

import numpy as np

Next, you should create a molecule. In the usual Psi4 input file, the molecule is designated in the molecule block. From within Python, the syntax looks only slightly different:

# set molecule mol = psi4.geometry(""" o h 1 1.0 h 1 1.0 2 104.5 symmetry c1 """)

The way you set options through Python is similar to how you would set them in a Psi4 input file, but the syntax is, again, slightly different:

psi4.set_options({'basis': 'sto-3g', 'scf_type': 'pk', 'e_convergence': 1e-8, 'd_convergence': 1e-8})

Now, we are ready to perform a Hartree-Fock computation to determine our reference electronic configuration:

# compute the Hartree-Fock energy and wave function scf_e, wfn = psi4.energy('SCF', return_wfn=True)

Note that we asked the energy routine to return a wave function. This object contains all of the important information from the Hartree-Fock computation, including the number of electrons, the number of orbitals, the AO/MO transformation matrices, and more! We will need some of this information for our CIS routine:

# Grab data from wavfunction class # number of doubly occupied orbitals ndocc = wfn.nalpha() # total number of orbitals nmo = wfn.nmo() # number of virtual orbitals nvirt = nmo - ndocc # Hartree-Fock energy e_scf = wfn.energy() # orbital energies eps = np.asarray(wfn.epsilon_a()) # occupied orbitals: Co = wfn.Ca_subset("AO", "OCC") # virtual orbitals: Cv = wfn.Ca_subset("AO", "VIR")

Now, we can use Psi4's MintsHelper class to generate the two-electron integrals that we need. The CIS Hamiltonian requires two classes of integrals of the type (ov|ov) and (oo|vv), where "o" represents an orbital that is occupied in the reference function, and "v" represents a virtual orbital. The MintsHelper class can construct tensors containing these specific classes of orbitals, provided we provide to it the corresponding definitions of the molecular orbitals (given by the Co and Cv matrices above):

# use Psi4's MintsHelper to generate ERIs mints = psi4.core.MintsHelper(wfn.basisset()) # build the (ov|ov) integrals: ovov = np.asarray(mints.mo_eri(Co, Cv, Co, Cv)) # build the (oo|vv) integrals: oovv = np.asarray(mints.mo_eri(Co, Co, Cv, Cv))

Given these tensors, you can access the element (ij|ab) in Python as oovv[i,j,a,b]. Here, the indices

*i*and

*j*could run from 0 to ndocc-1, and the indices

*a*and

*b*could run from 0 to nvirt-1.

For convenience, we can also strip out the occupied and virtual orbital energies from the full list of orbital energies that we obtained from the wave function object above:

# strip out occupied orbital energies, eps_o spans 0..ndocc-1 eps_o = eps[:ndocc] # strip out virtual orbital energies, eps_v spans 0..nvirt-1 eps_v = eps[ndocc:]

Now, we have all of the ingredients to build the CIS Hamiltonian! Using the expressions above, build the matrix representation of the Hamiltonian in the basis of spin-adapted singly-excited functions. The dimensions of this Hamiltonian should be ndocc*nvirt by ndocc*nvirt:

Ham = np.zeros((ndocc*nvirt,ndocc*nvirt))

The composite index that represents each basis function (

*φ*), can be expressed in terms of the orbitals that define that basis function. For example, the index ia = i*nvirt + a, where i=0..ndocc-1, and a=0..nvirt-1. Check your Hamiltonian matrix.

_{i}^{a}Once you have your Hamiltonian, you can diagonalize it using numpy's "eigvals" function:

# diagonalize Hamiltonian eig = np.linalg.eigvals

Note that the eigenvalues this function returns will not be sorted. You should write a small bit of code in Python that sorts them from low to high. Once you have done that, you can check your CIS excitation energies. (Note that these reference energies are given in eV, not Hartrees).

Step 2. Triplet excitation energies

We can also define spin-adapted basis functions for triplet spin states. The triplet spin-adapted basis functions are given by

and the corresponding matrix elements of the Hamiltonian are

Repeat this exercise and check your triplet CIS excitation energies, which, again, are given in units of eV.