Dielectric constant predictions for energetic materials using quantum calculations
Bibliographic record
Abstract
The dielectric constant (DC) is one of the key properties for detection of threat materials such as Improvised Explosive Devices (IEDs). In the present paper, the density functional theory (DFT) as well as ab-initio approaches are used to explore effective methods to predict dielectric constants of a series of 12 energetic materials (EMs) for which experimental data needed to experimentally determine the dielectric constant (refractive indices) are available. These include military grades energetic materials, nitro and peroxide compounds, and the widely used nitroglycerin. Ab-initio and DFT calculations are conducted. In order to calculate dielectric constant values of materials, potential DFT functional combined with basis sets are considered for testing. Accuracy of the calculations are compared to experimental data listed in the scientific literature, and time required for calculations are both evaluated and discussed. The best functional/basis set combinations among those tested are CAM-B3LYP and AUG-cc-pVDZm, which provide great results, with accuracy deviations below 5% when calculated results are compared to experimental data.
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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 | 0.001 |
| 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) | 0.003 | 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".