PyGSC: A Python tool for correcting Kohn-Sham orbital energies by mitigating the delocalization error of density functional approximations

TL;DR

PyGSC is an open-source Python tool that improves the accuracy of density functional theory calculations by correcting delocalization errors, leading to better predictions of electronic properties in molecules.

Contribution

The paper introduces a theoretical refinement of QE-DFT and a new Python implementation that enhances the accuracy of electron affinity and ionization potential predictions.

Findings

Modified QE-DFT outperforms original DFAs with mean absolute deviations below 0.3 eV.

Third-order corrections significantly improve prediction accuracy.

Application to nucleobases validates the method's effectiveness for large systems.

Abstract

Density functional approximations (DFAs) suffer from delocalization error, which limits their accuracy in predicting electron affinities (EAs), ionization potentials (IPs), and quasiparticle energies. In this work, we present a theoretical refinement of the quasiparticle energies from density functional theory (QE-DFT) method by improving the perturbative expression for the exchange-correlation potential, leading to a more consistent description of molecular systems. We further develop an open-source Python program, PyGSC, built upon the PySCF library, which implements the modified QE-DFT framework. Benchmark tests on main-group atoms and G2/97 molecules demonstrate that the modified QE-DFT method outperforms the original DFAs, with third-order corrections achieving mean absolute deviations below 0.3 eV for EA and IP predictions. Application to dipole-bound states of DNA/RNA nucleobases further validates the superiority of the QE-DFT approach over original DFAs, offering an efficient and accurate approach for predicting electronic properties in large molecular systems.