Multireference general-model-space state-universal and state-specific coupled-cluster approaches to excited states
Bibliographic record
Abstract
The concept of C-conditions, originally introduced in the framework of the multireference (MR), general-model-space (GMS), state-universal (SU), coupled-cluster (CC) approach with singles and doubles (GMS-SU-CCSD) to account for the internal amplitudes that vanish in the case of a complete model space, is applied to a state-selective or state-specific Mukherjee MR-CC method (MkCCSD). In contrast to the existing applications, the emphasis is on the description of excited states, particularly those belonging to the same symmetry species. The applicability of the C-conditions in all MR-SU-CC approaches is emphasized. Convergence problems encountered in the MkCCSD method when handling higher-lying states are pointed out. The performance of the GMS-SU-CCSD and MkCCSD methods is illustrated by considering low-lying vertical excitation energies of the ethylene molecule and para-benzyne diradical. A comparison with the equation-of-motion CCSD results, as well as with the available experimental data and recent multireference configuration interaction theoretical results, is also provided.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".