esipython 1.0.5

Calculation of electronic aromaticity indicators

The ESIpy program is aimed at the calculation of population analysis and aromaticity indicators from different Hilbert-space partitions using the PySCF module. The program supports both restricted and unrestricted calculations for single-determinant wavefunctions, and correlated wavefunctions from a restricted object (RHF). The atomic partitions supported by the program are Mulliken, Löwdin, meta-Löwdin, Natural Atomic Orbitals (NAO), and Intrinsic Atomic Orbitals ( IAO).

The on-line documentation can be found here.

Citation

All the calculations performed for the creation and implementation of this program have been conducted in the following scientific paper:

Joan Grèbol-Tomàs, Eduard Matito, Pedro Salvador, Chem. Eur. J. 2024, 30, e202401282.

Also, find it on-line here. If you are publishing the results obtained from ESIpy, remember to cite the program. The code is licensed under the GNU GPLv3. See the LICENSE file for details. See the on-line documentation for details on how to use the program. If you encounter any bugs, please feel free to report them on the Issues page, or send an email to [e-mail].

Installation

ESIpy can be installed through:

pip install esipython

The latest stable version can be obtained through:

pip install --upgrade esipython

The latest non-stable version available on Github can be obtained through:

pip install --upgrade git+https://github.com/jgrebol/ESIpy.git

For a detailed explanation on how to run the code and how to customize it, please see the documentation.

Getting started

ESIpy works on the object ESI, which will contain all the information required for the calculation. It is recommended to initialize the object with all the data, rather than adding it once the initialization process is finished.

The simplest form of input follows a usual PySCF calculation

    from pyscf import gto, dft
    import esipy

    mol = gto.Mole()
    mol.atom = '''
    6        0.000000000      0.000000000      1.393096000
    6        0.000000000      1.206457000      0.696548000
    6        0.000000000      1.206457000     -0.696548000
    6        0.000000000      0.000000000     -1.393096000
    6        0.000000000     -1.206457000     -0.696548000
    6        0.000000000     -1.206457000      0.696548000
    1        0.000000000      0.000000000      2.483127000
    1        0.000000000      2.150450000      1.241569000
    1        0.000000000      2.150450000     -1.241569000
    1        0.000000000      0.000000000     -2.483127000
    1        0.000000000     -2.150450000     -1.241569000
    1        0.000000000     -2.150450000      1.241569000
    '''
    mol.basis = 'sto-3g'
    mol.spin = 0
    mol.charge = 0
    mol.symmetry = True
    mol.verbose = 0
    mol.build()

    mf = dft.KS(mol)
    mf.kernel()

    ring = [1, 2, 3, 4, 5, 6]
    arom = esipy.ESI(mol=mol, mf=mf, rings=ring, partition="nao")
    arom.print()

To avoid the single-point calculation, the attribute save will save the AOMs and a dictionary containing information about the molecule and calculation into a binary file in disk. Hereafter, these will be accessible at any time. It is also recommended to use a for-loop scheme for all the partitions, as the computational time to generate the matrices is minimal and independent of the chosen scheme.

    ring = [1, 2, 3, 4, 5, 6]
    name = "benzene"
    for part in ["mulliken", "lowdin", "meta_lowdin", "nao", "iao"]:
        arom = esipy.ESI(mol=mol, mf=mf, rings=ring, partition=part, save=name)
        arom.print()

Additionally, one can generate a directory containing the AOMs in AIMAll format. These files are readable from ESIpy, but also from Eduard Matito's ESI-3D code. These are written through the method writeaoms():

    arom = esipy.ESI(mol=mol, mf=mf, rings=[1,2,3,4,5,6], partition="nao")
    arom.writeaoms("benzene")

Further work

• Approximations for the MCI calculation in large systems.
• Read the AOMs (or the data required for their calculation) from other source programs and store them as ESIpy Smo.
• Split the calculation into orbital contributions.
• Adaptation of some indicators to non-closed circuits (e.g., linear chains).