{\Delta}SCF Excitation Energies Up a Ladder of Ground-State Density Functionals
Ethan Pollack, Rohan Maniar, John P. Perdew

TL;DR
This paper evaluates the performance of various density functionals in calculating excitation energies using the $ riangle$SCF method across different atomic systems, highlighting improvements and limitations in excited-state predictions.
Contribution
It systematically assesses the $ riangle$SCF method with LSDA, PBE, and SCAN/r2SCAN functionals for excited states of atoms, revealing their accuracy and limitations.
Findings
Significant improvement in excitation energies from LSDA to SCAN for hydrogen.
Effective mass differs from the bare mass only with r2SCAN in the uniform gas.
Reasonably accurate excitation energies for non-spin-flip cases, less so for spin-flip cases.
Abstract
Density functional theory (DFT) is a widespread and effective tool in electronic structure calculations for ground-state electron systems. Its success has prompted exploration into the use of DFT for non-collective excited states. The delta self-consistent field (SCF) method allows for the extension of DFT to excited-state energies by restricting the Kohn-Sham orbital occupations, producing an excited-state electron density, and then computing its energy. In this paper, we examine the performance of the LSDA, PBE generalized-gradient approximation (GGA), and SCAN/r2SCAN meta-GGA for the excitation energies of several important systems. We consider the energies of atoms with atomic number 1-18. For the hydrogen atom, where we use the exact electron density and have no multiplet splitting, we find significant improvement up the ladder from LSDA to PBE to SCAN. For the uniform gas,…
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.
Taxonomy
TopicsAdvanced Chemical Physics Studies · Advanced Physical and Chemical Molecular Interactions · Machine Learning in Materials Science
