A parallelized molecular collision cross section package with optimized accuracy and efficiency
Bibliographic record
Abstract
Ion mobility-based separation prior to mass spectrometry has become an invaluable tool in the structural elucidation of gas-phase ions and in the characterization of complex mixtures. Application of ion mobility to structural studies requires an accurate methodology to bridge theoretical modelling of chemical structure with experimental determination of an ion's collision cross section (CCS). Herein, we present a refined methodology for calculating ion CCS using parallel computing architectures that makes use of atom specific parameters, which we have called MobCal-MPI. Tuning of ion-nitrogen van der Waals potentials on a diverse calibration set of 162 molecules returned a RMSE of 2.60% in CCS calculations of molecules containing the elements C, H, O, N, F, P, S, Cl, Br, and I. External validation of the ion-nitrogen potential was performed on an additional 50 compounds not present in the validation set, returning a RMSE of 2.31% for the CCSs of these compounds. Owing to the use of parameters from the MMFF94 forcefield, the calibration of the van der Waals potential can be extended to additional atoms defined in the MMFF94 forcefield (i.e., Li, Na, K, Si, Mg, Ca, Fe, Cu, Zn). We expect that the work presented here will serve as a foundation for facile determination of molecular CCSs, as MobCal-MPI boasts up to 64-fold speedups over traditional calculation packages.
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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 | 0.002 | 0.004 |
| 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 | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".