Mechanical equilibrium, a prerequisite to unveil auxetic properties in molecular compounds
Bibliographic record
Abstract
The unique physical properties of auxetic compounds make them very attractive materials. Nevertheless, no synthesised materials known to exhibit negative Poisson’s ratio at the molecular level have been made. One way to explore such compounds is to predict potential candidates prior to their synthesis. To achieve it, multi-atom simulation is a powerful predicting tool. However, the lack of existing systems means that the crucial step of validation cannot be carried out. This paper aims to provide a procedure to predict the auxeticity of proposed molecular systems. The strategy is based on first revealing the determinant step in reaching the mechanical equilibrium of existing isotropic compounds such as polymers, or organic glasses, to compute efficiently mechanical properties. The Poisson’s ratio is found in good agreement with experimental data. The procedure is thus modified to be applied to anisotropic compounds, liquid crystals. The agreement with experimental behaviour allowed us to extrapolate the procedure to potentially auxetic compounds, thus offering great opportunities to reveal auxetic properties prior to the synthesis of the molecules.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".