Top-bending deformation in silver nanowires: Insights from molecular dynamics and autonomous basin climbing simulations
Bibliographic record
Abstract
Atomistic insight into nanowire deformation under mechanical loading is necessary to bridge the gap between theoretical modeling and real-world nanoscale applications. In this study, we investigate the bending-induced plasticity of single-crystalline silver nanowires using molecular dynamics (MD) and autonomous basin climbing (ABC) simulations. Top-bending tests were performed along both [111] and [001] crystallographic orientations to explore the role of applied force, boundary conditions, and timescale sensitivity on defect formation. At low applied forces, MD simulations predicted purely elastic behavior, while ABC revealed early plastic activity, including the nucleation of stacking faults near the fixed end and the formation of twin boundaries. These plastic events emerged well below the MD yield threshold, enabled by ABC’s ability to access long-timescale, diffusion-mediated mechanisms such as surface atom rearrangement and barrier-lowering via atomic shuffling. Under elevated forces, both MD and ABC captured the formation of stable five-fold twin structures, though only ABC simulations revealed their nucleation sequence and internal development in detail. These findings underscore ABC’s essential role in resolving thermally activated deformation pathways that are inaccessible to conventional MD. By bridging the timescale gap, ABC provides critical insight into early-stage plasticity and defect evolution in nanoscale metals, offering a more comprehensive understanding of deformation under bending.
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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 | 0.000 |
| 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".