Simple Linear Regression Models for Prediction of Ionization Energies, Electron Affinities, and Fundamental Gaps of Atoms and Molecules

Linear regression equations were developed for different density functionals using data from the CCCBDB, along with a test set of 89 ionization energies (IE) and 76 electron affinities (EA) so that experimental IE and EA can be predicted from orbital energies. Separate equations were determined for different classes of atoms and molecules. These relationships were also applied to all occupied orbitals to simulate the photoemission spectra of organic molecules with accuracy similar to that of other computational methods at a fraction of the cost. The error for large molecules (up to 200 atoms) can be below 0.2 eV with many functionals for the prediction of the IE and EA.

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Publication Details

Journal
Journal of Chemical Theory and Computation
Published
2025-03-26
DOI
https://doi.org/10.1021/acs.jctc.4c01591
Citations
1
Primary Topic
Mass Spectrometry Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.76
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article

Simple Linear Regression Models for Prediction of Ionization Energies, Electron Affinities, and Fundamental Gaps of Atoms and Molecules

Rebecca K. Carlson
1 citations
Journal of Chemical Theory and Computation
Mass Spectrometry Techniques and Applications
0.76
article

Simple Linear Regression Models for Prediction of Ionization Energies, Electron Affinities, and Fundamental Gaps of Atoms and Molecules

Rebecca K. Carlson
article en
1 citations

Abstract

Linear regression equations were developed for different density functionals using data from the CCCBDB, along with a test set of 89 ionization energies (IE) and 76 electron affinities (EA) so that experimental IE and EA can be predicted from orbital energies. Separate equations were determined for different classes of atoms and molecules. These relationships were also applied to all occupied orbitals to simulate the photoemission spectra of organic molecules with accuracy similar to that of other computational methods at a fraction of the cost. The error for large molecules (up to 200 atoms) can be below 0.2 eV with many functionals for the prediction of the IE and EA.

Journal of Chemical Theory and ComputationVol. 21(7)
National Institute of Standards and Technology (US), National Institute of Standards (EG), NOAA Chemical Sciences Laboratory (US)
Openalex Percentile: Top 33%
Mass Spectrometry Techniques and Applications
0.76
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Simple Linear Regression Models for Prediction of Ionization Energies, Electron Affinities, and Fundamental Gaps of Atoms and Molecules — Rebecca K. Carlson · Journal of Chemical Theory and Computation (2025) | TGRS Research Map | TGRS