Theoretical Prediction of Heats of Sublimation of Energetic Materials Using Pseudo‐Atomic Orbital Density Functional Theory Calculations
Bibliographic record
Abstract
Abstract We present a predictive model for the heats of sublimation of the condensed phases of energetic materials that combine the empirical relations of Politzer with first‐principles density‐functional calculations of the electronic properties of the molecular surfaces. The distinct features of our methodology are the use of numerical pseudo‐atomic orbitals for the quantum mechanical calculation of the electronic charge density, as well as an improved technique for the molecular surface area determination. As applications, we used our model to predict heats of sublimation of energetic molecules CL‐20, HMX, RDX, TNT, FOX‐7, TATB, and LLM‐105, with the Politzer parameters fit based on a set of eight nitro‐aromatic molecules. In comparison with conventional quantum chemistry calculations, our approach is tremendously less computationally demanding, yet it still demonstrates competitive accuracy and predictive power.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".