mqcomplab / DonorNumberPrediction

Predicting experimental donor number values using conceptual Density Functional Theory methods.

Repository from Github https://github.commqcomplab/DonorNumberPredictionRepository from Github https://github.commqcomplab/DonorNumberPrediction

DonorNumberPrediction

About

DonorNumberPrediction allows to calculate donor number values using conceptual Density Functional Theory methods. The basic theory is detailed in: "Calculation of donor numbers: computational estimates for the Lewis basicity of solvents.", R. A. Miranda-Quintana, J. Smiatek; J. Mol. Liquids (submitted)

License

DonorNumberPrediction is distributed under GPL License version 3 (GPLv3).

Dependencies

Python >= 3.3; http://www.python.org/

Numpy >= 1.9.1; http://www.numpy.org/

SciPy >= 0.11.0; http://www.scipy.org/

Matplotlib >= 1.0; http://matplotlib.org/

Usage

The file donor_number_fit.py contains the class DonorNumber which is used to process the data, fit the models, and estimate their errors. The file donor_number_out.py contains auxiliary functionality that can be used to process the information contained in a DonorNumber instance. The experimental DN values must be provided in a separate file containing three columns separated by ";":

inert_solvent;reference_acid;solvent;donor_number

The file DN_data.csv is an example containing the data used in "Calculation of donor numbers: computational estimates for the Lewis basicity of solvents.", R. A. Miranda-Quintana, J. Smiatek; J. Mol. Liquids (submitted). To further reference this data please also cite: "Enthalpic contributions to solvent–solute and solvent–ion interactions: Electronic perturbation as key to the understanding of molecular attraction", J. Smiatek, J. Chem. Phys., 150, 174112, (2019).

Currently, the ionization energies and electron affinities of the inert solvent, reference acid, and solvents, are included as dictionaries in the donor_number_out.py file.

Reference

Please, cite both the associated manuscript:

"Calculation of donor numbers: computational estimates for the Lewis basicity of solvents.", R. A. Miranda-Quintana, J. Smiatek; J. Mol. Liquids (submitted)

And this repository:

DOI

Further reading

Some relevant references are:

1- C-DFT-based solvation models:

"Enthalpic contributions to solvent–solute and solvent–ion interactions: Electronic perturbation as key to the understanding of molecular attraction", J. Smiatek, J. Chem. Phys., 150, 174112, (2019).

"Specific Ion Effects and the Law of Matching Solvent Affinities: A Conceptual Density Functional Theory Approach", J. Smiatek, J. Phys. Chem. B, 124, 2191, (2020).

2- Perturbations in C-DFT:

"Fractional electron number, temperature, and perturbations in chemical reactions", R. A. Miranda-Quintana, P. W. Ayers, Phys. Chem. Chem. Phys., 18, 15070, (2016).

"Perturbed reactivity descriptors: the chemical hardness", R. A. Miranda-Quintana, Theo. Chem. Acc., 136, 76, (2017).

About

Predicting experimental donor number values using conceptual Density Functional Theory methods.

License:GNU General Public License v3.0


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Language:Python 100.0%