Multireference coupled-cluster approaches to excited states
Bibliographic record
Abstract
We review our recent work on multireference (MR) coupled-cluster (CC) methodology, namely (i) the idea of external corrections that are essential for the so-called reduced MR (RMR) CCSD and RMR-CCSD(T) methods at the single-reference (SR) level and for (N,M)-CCSD at the MR level, and (ii) the concept of the so-called C-conditions for the internal cluster amplitudes at the genuine MR CC state universal (SU) level. The latter concept enables the use of general model spaces (GMSs) while preserving the intermediate normalization and can be employed in any MR CC method that is based on the SU cluster Ansatz of Jeziorski and Monkhorst, including state-selective (SS) approaches, such as the Mukherjee MkCCSD and Brillouin-Wigner BW-CC methods. The performance of the RMR-CCSD(T), GMS-SU-CCSD, GMS-MkCCSD, and (N,M)-CCSD approaches is illustrated on a few typical examples.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".