Evidence map›Paper›PMID 42875438›Full record

ArticleNature communications2026

Improving coupled cluster theory for strongly correlated molecules with Kohn-Sham density encoding.

Abdulrahman Y Zamani, Barbaro Zulueta, Andrew M Ricciuti, John A Keith, Kay Carter-Fenk

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Abdulrahman Y ZamaniDepartment of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA.ORCID http://orcid.org/0000-0002-7680-174X
Barbaro ZuluetaDepartment of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
Andrew M RicciutiDepartment of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA.
John A KeithDepartment of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA. jakeith@pitt.edu.ORCID http://orcid.org/0000-0002-6583-6322
Kay Carter-FenkDepartment of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA. kay.carter-fenk@pitt.edu.ORCID http://orcid.org/0000-0001-8302-4750

Funding

National Science Foundation (NSF) OAC-2117681United States Department of Defense | United States Navy | U.S. Naval Research Laboratory (United States Naval Research Laboratory) N00173-25-1-0040
6 · The paper itself

Abstract

Coupled cluster theory using a Kohn-Sham density functional reference can dramatically outperform the standard Hartree-Fock-based approach for strongly correlated systems, but the origin of these improvements has remained unclear. We show these improvements arise from differences in the one-particle density as encoded into the Fock matrix, not from the nature of the orbitals themselves, as is commonly assumed. Exploiting this insight, Kohn-Sham references enable near-chemical accuracy for electronic and thermochemical properties of transition metal dimers and main group compounds. Notably, Kohn-Sham coupled cluster qualitatively recovers the entire chromium dimer potential energy surface, a notorious failure case for conventional methods. We further introduce a density difference diagnostic that detects multireference character and guides the selection of optimal references at mean-field cost. Together, these results establish a practical route to treating strong correlation within quantum chemistry's "gold standard" framework, with immediate implications for machine learning potential development and materials research, which rely heavily on density functional theory.

Identifiers

PMID42875438
PMCPMC13669318

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.