Direct iterative solution of the generalized Bloch equation. III. Application to H2-cluster models
Bibliographic record
Abstract
A state-selective multiconfigurational single-reference method that was outlined in the preceding paper of this series (H. Meißner and J. Paldus, J. Chem. Phys. 113, 2594 (2000); preceding paper), and is based on a quadratic iterative algorithm enabling the direct solution of the generalized Bloch equation, is applied to several model systems consisting of interacting hydrogen molecules, nowadays referred to as the H4, S4, and H8 models. These exactly solvable models are often used to test the efficacy of post-Hartree–Fock methods in their ability to recover both the dynamic and nondynamic correlation energies, since they enable a continuous variation of the degree of quasidegeneracy from the degenerate to nondegenerate limit by varying a single geometrical parameter, while simulating the dissociation of one or more single bonds. Various approximation schemes that were outlined in Part II, as well as their combinations, are tested and their performance evaluated. The size-extensivity deviations of those approximations that do not rely on the exponential cluster ansatz for the wave operator are also examined using larger hydrogen molecule clusters. It is shown that the so-called BQ4 approximation performs extremely well in all cases and even outperforms the externally corrected, reduced multireference (RMR) CCSD in the quasidegenerate region of geometries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".