Predicting Shock Sensitivity from Differential Scanning Calorimetry Data and Molecular Structure: Beyond the Yoshida Correlation
Bibliographic record
Abstract
The Yoshida correlation is widely used in the pharmaceutical and fine chemical industry to predict explosivity and shock sensitivity of chemical substances based on the initiation temperature and enthalpy of differential scanning calorimetry (DSC) exotherms. We investigate the origins and accuracy of this correlation (and commonly used modifications thereof) by applying it to a large data set of 383 compounds, which are relevant to the pharmaceutical industry, and demonstrate that the initiation temperature and enthalpy variables are not good predictors for shock sensitivity. By incorporating structural information (for the 292 compounds where it was available), we used machine learning to inform and guide a logistic regression technique to develop a shock sensitivity model which has a higher overall accuracy (63%) and a higher accuracy for shock-sensitive compounds (97%) compared to the original Yoshida correlation (52% overall accuracy, 82% accuracy for shock-sensitive compounds). This logistic regression model includes both the original Yoshida variables (DSC initiation temperature and enthalpy) and also incorporates the oxygen balance (OB 100 ) and the number of energetic nitrogen groups in the molecule.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".