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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".