Prediction of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>o</mml:mi> <mml:mo>−</mml:mo> <mml:msub> <mml:mi mathvariant="normal">N</mml:mi> <mml:mn>16</mml:mn> </mml:msub> </mml:mrow> </mml:math> : A layered polymeric nitrogen phase
Bibliographic record
Abstract
Nitrogen allotropes, especially under extreme conditions, play a crucial role in understanding atomic configurations and driving innovations in high-energy materials. The phase composition of layered polymeric nitrogen (LP-N) remains unresolved, with Pba2, $C2$/c, and Pccn structures proposed as candidates. However, their relative stabilities and ambiguous x-ray-diffraction (XRD) patterns---consistent with experimental data but not definitely---along with multiple possible explanations, have been topics of ongoing debate, particularly under varying pressures. In this study, we present an unexpected orthorhombic phase, $o\ensuremath{-}{\mathrm{N}}_{16}$, predicted using machine-learning driven swarm-intelligence approach. Gibbs free-energy calculations, kinetic barrier analysis during decompression, and simulated XRD patterns reveal $o\ensuremath{-}{\mathrm{N}}_{16}$ and Pba2 as dominant LP-N phases at elevated pressures, with a partial transition from $o\ensuremath{-}{\mathrm{N}}_{16}$ to $C2$/c upon decompression. Notably, the relative stabilities of nitrogen allotropes cg-N, $o\ensuremath{-}{\mathrm{N}}_{16}, C2$/c, and Pccn are dictated by configurational and conformational isomerism of armchair chains and interchain bonding. These findings shed light on the evolution of atomic configurations under high-pressure conditions. Beyond its favorable energy density, $o\ensuremath{-}{\mathrm{N}}_{16}$ exceeds all polymeric nitrogen structures in terms of detonation velocity and detonation pressure.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".