Correction for Triples in Reduced Multireference Coupled-Cluster Approaches
Bibliographic record
Abstract
The performance of the recently proposed version of the reduced multireference (RMR) coupled-cluster (CC) method with singles and doubles (SD), which employs a modest-size configuration interaction wave function as an external source for a small subset of approximate connected three- and four-body cluster amplitudes that are primarily responsible for the nondynamic correlation effects, and which has been perturbatively corrected for the remaining triples along the same line as in the standard CCSD(T) method (Li X., Paldus J.: J. Chem. Phys. 2006, 124, 174101), referred to by the acronym RMR CCSD(T), is being tested by evaluating equilibrium spectroscopic constants for a demanding system of the beryllium dimer, as well as by computing atomization energies for several di- and triatomics. The focus is on the equilibrium properties, since it has been demonstrated earlier that the RMR CCSD method corrects well for the nondynamic correlation in bond-breaking situations. We find that in all the cases we have examined, the RMR CCSD(T) method does in fact improve the performance of CCSD(T) even in the vicinity of the equilibrium geometry. For states possessing a moderate multireference character, the improvement in computed thermochemical properties relative to CCSD(T) amounts to a few kJ/mol, a meaningful amount when striving for chemical accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".