Accurate Electron Affinities and Orbital Energies of Anions from a Non-Empirically Tuned Range-Separated Density Functional Theory Approach
Lindsey N. Anderson, M. Bel\'en Oviedo, and Bryan M. Wong

TL;DR
This paper demonstrates that a non-empirically tuned range-separated density functional theory approach accurately predicts electron affinities and orbital energies of atomic anions across the periodic table, resolving longstanding issues with positive HOMO energies.
Contribution
It introduces a non-empirically tuned range-separated DFT method that provides accurate, self-consistent electronic properties for anions, outperforming previous non-self-consistent approaches.
Findings
Best accuracy achieved with the non-empirically tuned approach.
Method yields well-defined electronic couplings and gradients.
Corrects the positive HOMO energy issue in anion calculations.
Abstract
The treatment of atomic anions with Kohn-Sham density functional theory (DFT) has long been controversial since the highest occupied molecular orbital (HOMO) energy, , is often calculated to be positive with most approximate density functionals. We assess the accuracy of orbital energies and electron affinities for all three rows of elements in the periodic table (H-Ar) using a variety of theoretical approaches and customized basis sets. Among all of the theoretical methods studied here, we find that a non-empirically tuned range-separated approach (constructed to satisfy DFT-Koopmans' theorem for the anionic electron system) provides the best accuracy for a variety of basis sets - even for small basis sets where most functionals typically fail. Previous approaches to solve this conundrum of positive values have utilized non-self-consistent methods; however…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
