Efficient parameterization of torsional terms for force fields
Bibliographic record
Abstract
A novel method is presented for fitting force-field dihedral angles using an ensemble of structures generated from an ab initio Monte Carlo simulation. Importance sampling is used to achieve an efficient algorithm using a low level of theory to minimize the system at each step with the dihedral angles constrained, followed by dihedral fitting using the single point energies at a higher level of theory. The resulting method is an order of magnitude more efficient than the traditional method of doing a constrained scan over each dihedral independently. Also as the sampling is more uniformly distributed, the full surface is approximated to a greater accuracy. The dihedral fitting is done with a nonlinear optimization method to vary the phase as well as the force constant. The utility of the method is demonstrated by fitting dihedrals of methyl L-lactate, diisopropyl fluorophosphate, isopentenyl phosphate, a leucine dipeptide, and two inhibitors of Signal Transducer and Activator of Transcription 5. The results show that the Monte Carlo scheme is more efficient than constrained scans and is particularly effective at approximating the underlying potential energy surface when the dihedral degrees are coupled.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".