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Record W2606175526 · doi:10.1063/1.4979078

Excited states from modified coupled cluster methods: Are they any better than EOM CCSD?

2017· article· en· W2606175526 on OpenAlexafffund
Varun Rishi, Ajith Perera, Marcel Nooijen, Rodney J. Bartlett

Bibliographic record

VenueThe Journal of Chemical Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsUniversity of Waterloo
FundersAir Force Office of Scientific ResearchNatural Sciences and Engineering Research Council of Canada
KeywordsExcited stateCoupled clusterRydberg formulaGround stateAtomic physicsPhysicsSinglet stateQuantum mechanicsIonizationMolecule

Abstract

fetched live from OpenAlex

Simplifications or modifications of coupled cluster methods such as the CCSD (coupled cluster singles and doubles) model often perform better than the original method in providing the total energy, equilibrium geometries, and harmonic vibration frequencies for the ground state. Three such methods that have been recently proposed include 2CC, parameterized CCSD generalizations, and distinguishable cluster singles and doubles (DCSD) approach. In this paper, we lay the theoretical foundation needed to treat excited states via the equation of motion (EOM) approach using these ground state CC methods. As these ground state approximations to CCSD share its property of being exact for two-electron systems, so will their excited state extensions. These methods are tested for two complementary benchmark sets of excited states for a wide range of organic molecules with focus on singlet and triplet excited states of both valence and Rydberg nature. We also test these methods for doubly excited states, taking CH+ as an example to test their performance at equilibrium and stretched bond geometries. Finally, we assess if any of these methods perform consistently better than EOM CCSD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.313
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations47
Published2017
Admission routes2
Has abstractyes

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