A Machine-Learned “Chemical Intuition” to Overcome Spectroscopic Data Scarcity
Bibliographic record
Abstract
Machine learning models for predicting IR spectra of molecular ions (infrared ion spectroscopy, IRIS) have yet to be reported owing to the relatively sparse experimental data sets available. To overcome this limitation, we employ the Graphormer-IR model for neutral molecules as a knowledgeable starting point and then employ transfer learning to refine the model to predict the spectra of gaseous ions. A library of 10,336 computed spectra and a small data set of 312 experimental IRIS spectra is used for model fine-tuning. Nonspecific global graph encodings that describe the molecular charge state ( i.e ., (de)protonation, sodiation), combined with an additional transfer learning step that considers computed spectra for ions, improved model performance. The resulting Graphormer-IRIS model yields spectra that are 21% more accurate than those produced by commonly employed DFT quantum chemical models, while capturing subtle phenomena such as spectral red-shifts due to sodiation. Dimensionality reduction of model embeddings demonstrates derived “chemical intuition” of functional groups, trends in molecular electron density, and the location of charge sites. Our approach will enable fast IRIS predictions for determining the structures of unknown small molecule analytes ( e.g ., metabolites, lipids) present in biological samples.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".