Innovative modeling of monolayer puckered arsenene: Bridging quantum mechanics and finite element analysis
Bibliographic record
Abstract
Current study presents a novel hybrid approach combining finite element modeling and density functional theory calculations to investigate the mechanical properties of monolayer puckered arsenene. The multiscale analysis in this study leverages finite element analysis as a distinctive approach, complementing the nano‐scale capabilities of density functional theory and molecular dynamics by overcoming limitations faced by these two methods in representing complex scenarios. Furthermore, finite element analysis demonstrates computational efficiency for larger structures, making it suitable for systems where atomistic simulations may be impractical. This hybrid methodology offers a unique framework for accurately predicting key properties, including elastic modulus and buckling force, by synergistically integrating the strengths of both computational techniques. In addition to demonstrating the effectiveness of our approach in accurately capturing material behavior, our findings shed light on fundamental aspects of nanoscale mechanics, with implications for various applications in nanotechnology, materials science, and structural engineering. By providing a deeper understanding of the mechanical response of 2D materials, our research contributes to advancing the field of nanoscale materials engineering and informs the design of innovative nanostructures with tailored mechanical properties.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".