General-model-space state-universal coupled-cluster methods for excited states: Diagonal noniterative triple corrections
Bibliographic record
Abstract
The recently developed multireference, general-model-space, state-universal coupled-cluster approach considering singles and doubles (GMS SU CCSD) has been extended to account perturbatively for triples, similar to the ubiquitous single-reference CCSD(T) method. The effectiveness of this extension in handling of excited states and its ability to account for the static and nondynamic correlation effects when considering spin- and/or space-symmetry degenerate levels within the spin-orbital formalism is examined on the example of low-lying excitation energies of the C2, N2, and CO molecules and a comparison is made with the (N,N)-CCSD method used for the same purpose. It is shown that while the triple corrections are very effective in improving the absolute energies, they have only a modest effect on the corresponding excitation energies, which may be even detrimental if both the ground- and excited-state levels cannot be given a balanced treatment. While the triple corrections help to avoid the symmetry-breaking effects arising due to the use of the spin-orbital formalism, they are much less effective in this regard than the (N,N)-CCSD approach.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".