Optimizing Structural and Mechanical Properties of Coiled Carbon Nanotubes with NSGA-II and Reactive Molecular Dynamics Simulation
Bibliographic record
Abstract
Coiled carbon nanotubes (CCNTs) have increasingly become a vital factor in the new generation of nanodevices and energy-absorbing materials due to their outstanding properties. Here, the multiobjective optimization of CCNTs is applied to assess their mechanical properties. The best trade-off between conflicting mechanical properties (e.g., yield stress and yield strain) is demonstrated and the optimization of the geometry enables us to find the astonishing CCNTs with a stretchability of 400%. These structures have been recognized for the first time in the field. We derived several highly accurate analytical equations for the yield stress and yield strain by the implementation of multiobjective optimization and fitting a theoretical model to the results of molecular dynamics (MD) simulations. The optimized structures are highly resilient because of two distinct deformation mechanisms depending on the dimensions of CCNTs. For small CCNTs, extraordinary extensibility is mainly contributed by buckling and nanohinge-like deformation with maintaining the inner coil diameter. On the other hand, for large CCNTs, this is accomplished by the creation of a straight CNT-like structure in the inner-edge of the CCNT with a helical graphene ribbon twisted around it. Our work represents an important advance in the design of CCNT based mechanical nanodevices.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 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".