Dielectric response of common explosives based on DFT-calculated IR spectra
Bibliographic record
Abstract
Detection of infrared (IR) spectrum features of target molecules can be achieved by comparison of experimentally measured spectra to template spectra within a database. The focus of this study is the scalability of density functional theory (DFT)-calculated IR spectra with respect to macroscales, characteristic of dielectric response as measured using IR spectroscopic methods, and demonstration that IR-spectrum databases can be constructed with respect to classes of target molecules, where DFT-calculated spectra provide a complementary extension of experimentally measured IR spectra. A case-study analysis concerning IR-spectra scalability for a set of common explosives, including TNT, is described. This analysis provides an example of calculating template spectra for potential detection of target molecules, where DFT-calculated IR spectra are frequently more convenient than laboratory measurements using IR spectroscopic methods. We have shown that DFT-calculated spectra can be included within template-spectrum databases, and their ability to show very good correlation with measured spectra.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".