Symbolic computation engines and molecular modeling templates: Maple‐assisted point group analysis of the vibrational activity of molecules
Bibliographic record
Abstract
The present study proposes an algorithm for point-group analysis (PGA) of the vibrational activity of molecules, adapted for the efficient utilization of the linear packages incorporated into currently available symbolic computation engines (SCE), such as Maple, Mathcad, or Mathematica. By the creation of this algorithm, we have addressed the need for a numerically friendly environment, outside the “locked” procedures within molecular modeling packages, which will preserve its flexibility, transparency, and maneuverability, regardless of the complexity of the calculation. The format of the character tables of the point groups significant to chemical species has been adapted to ensure automatic numerization, and consistent input of the alphanumeric data from the existent character tables into the SCE templates designed to perform the PGA. The two proposed templates address two complementary objectives: (i) a totally transparent and interactive file has been designed to allow access to all intermediate results at all levels of the procedure for easy implementation of potential additional modules of special interest 1-5, and (ii) for fast output and routine calculations of the IR/Raman vibrational activity of molecules based on their point groups, a totally automatic file with a highly simplified input interface has been designed. The numerical interface conveniently replaces the usual graphic user interface that is common to most commercial molecular modeling software packages, requiring minimum input determination. The structure for both templates is based on the use of the digitized forms for the character tables, for the symmetry operations, and for symmetry elements, all saved in dedicated libraries uploaded to the numerical database of the SCE. © 2005 Wiley Periodicals, Inc. Int J Quantum Chem, 2006
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".