Constructing high-accuracy intermolecular potential energy surface with multi-dimension Morse/Long-Range model
Bibliographic record
Abstract
Spectroscopically accurate Potential Energy Surfaces (PESs) are fundamental for explaining and making predictions of the infrared and microwave spectra of van der Waals (vdW) complexes, and the model used for the potential energy function is critically important for providing accurate, robust and portable analytical PESs. The Morse/Long-Range (MLR) model has proved to be one of the most general, flexible and accurate one-dimensional (1D) model potentials, as it has physically meaningful parameters, is flexible, smooth and differentiable everywhere, to all orders and extrapolates sensibly at both long and short ranges. The Multi-Dimensional Morse/Long-Range (mdMLR) potential energy model described herein is based on that 1D MLR model, and has proved to be effective and accurate in the potentiology of various types of vdW complexes. In this paper, we review the current status of development of the mdMLR model and its application to vdW complexes. The future of the mdMLR model is also discussed. This review can serve as a tutorial for the construction of an mdMLR PES.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".