State-Specific Coupled-Cluster Methods for Excited States
Yann Damour, Anthony Scemama, Denis Jacquemin, F\'abris, Kossoski, Pierre-Fran\c{c}ois Loos

TL;DR
This study evaluates the accuracy of the $ ext{Delta}$CCSD method for excited states, comparing it with EOM-CCSD across a diverse set of molecular excitations, highlighting its strengths and limitations.
Contribution
The paper provides a comprehensive benchmark of $ ext{Delta}$CCSD against EOM-CCSD for various excited states, including doubly excited and multiconfigurational states, revealing its relative performance.
Findings
$ ext{Delta}$CCSD underperforms EOM-CCSD for most excited states.
Doublet-doublet transitions show small error differences between methods.
Multiconfigurational character increases challenges for $ ext{Delta}$CCSD.
Abstract
We reexamine CCSD, a state-specific coupled-cluster (CC) with single and double excitations (CCSD) approach that targets excited states through the utilization of non-Aufbau determinants. This methodology is particularly efficient when dealing with doubly excited states, a domain where the standard equation-of-motion CCSD (EOM-CCSD) formalism falls short. Our goal here is to evaluate the effectiveness of CCSD when applied to other types of excited states, comparing its consistency and accuracy with EOM-CCSD. To this end, we report a benchmark on excitation energies computed with the CCSD and EOM-CCSD methods, for a set of molecular excited-state energies that encompasses not only doubly excited states but also doublet-doublet transitions and (singlet and triplet) singly-excited states of closed-shell systems. In the latter case, we rely on a minimalist version of…
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.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Chemical Physics Studies · Machine Learning in Materials Science · Spectroscopy and Quantum Chemical Studies
