j-IR-vis: Vision model for Infrared spectroscopy embeddings
Bibliographic record
Abstract
Infrared (IR) spectroscopy provides rich structural information, but interpreting spectra at scale remains challenging. Here, we introduce j-IR-vis, a vision model trained on IR spectra for functional-group prediction and downstream molecular characterization. Trained separately on simulated (Dsim) and experimental (Dexp) datasets, j-IR-vis achieves strong functional group classification accuracy, with exact-match ratios of 0.77-0.81 on Dsim and 0.67-0.76 on Dexp, and macro-F1 scores up to 0.93. Grad-CAM saliency maps reveal that the model tends to focus on the fingerprint region (1500-400 cm-1), consistent with the model being sensitive to vibrational features in this region. Embedding-space analyses show that the inclusion of this region improves all geometric metrics-RankMe, Silhouette, Separation, and Lift-indicating a richer and more structured latent manifold. Fixed embeddings further enable accurate prediction of aromatic ring counts (weighted F1 = 0.69) and octanol-water partition coefficients (RMSE = 1.18, Spearman's ρ = 0.59), demonstrating representation reuse for structural and physicochemical tasks without retraining the vision backbone. Finally, cosine-similarity analyses reveal that j-IR-vis captures complementary chemical relationships compared to traditional fingerprint-based representations. Together, these results establish j-IR-vis as a reusable spectral encoder that bridges experimental spectroscopy and molecular machine learning, offering a spectra informed route to multi-modal chemical representations. j-IR-vis is openly accessible through the GitHub repository https://github.com/ChemAI-Lab/jirvis.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".