Assessing the Reliability of Truncated Coupled Cluster Wavefunction: Estimating the Distance from the Exact Solution
\'Ad\'am Ganyecz, Zsolt Benedek, Kl\'ara Petrov, Gergely Barcza, Andr\'as Olasz, Mikl\'os A. Werner, \"Ors Legeza

TL;DR
This paper introduces a new wavefunction-based metric to estimate the deviation of truncated coupled cluster solutions from the exact FCI wavefunction, providing a practical reliability assessment tool.
Contribution
It proposes a novel, cost-effective diagnostic for quantifying the distance from the exact wavefunction, applicable to various wavefunction methods including CCSD.
Findings
The metric effectively distinguishes solutions far from the FCI wavefunction.
It is independent of common multireference diagnostics.
Application to CCSD shows reliable assessment on benchmark datasets.
Abstract
A new approach is proposed to assess the reliability of the truncated wavefunction methods by estimating the deviation from the full configuration interaction (FCI) wavefunction. While typical multireference diagnostics compare some derived property of the solution with the ideal picture of a single determinant, we try to answer a more practical question, how far is the solution from the exact one. Using the density matrix renormalization group (DMRG) method to provide an approximate FCI solution for the self-consistently determined relevant active space, we compare the low-level CI expansions and one-body reduced density matrixes to determine the distance of the two solutions (, ). We demonstrate the applicability of the approach for the CCSD method by benchmarking on the W4-17 dataset, as well as on transition metal-containing species. We also show…
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 · Machine Learning in Materials Science · Inorganic Fluorides and Related Compounds
