Atomistic modeling of electromechanical properties of piezoelectric zinc oxide nanowires
Bibliographic record
Abstract
Currently, numerous articles are devoted to examining the influence of geometry and charge distribution on the mechanical properties and structural stability of piezoelectric nanowires (NWs). The varied modeling techniques adopted in earlier molecular dynamics (MD) works dictated the outcome of the different efforts. In this article, comprehensive MD studies are conducted to determine the influence of varied interatomic potentials (partially charged rigid ion model, [PCRIM] ReaxFF, charged optimized many-body [COMB], and Buckingham), geometrical parameters (cross-section geometry, wire diameter, and length), and charge distribution (uniform full charges versus partially charged surface atoms) on the resulting mechanical properties and structural stability of zinc oxide (ZnO) NWs. Our optimized parameters for the Buckingham interatomic potential are in good agreement with the existing experimental results. Furthermore, we found that the incorrect selection of interatomic potentials could lead to excessive overestimate (61%) of the elastic modulus of the NW. While NW length was found to dictate the strain distribution along the wire, impacting its predicted properties, the cross-section shape did not play a major role. Assigning uniform charges for both the core and surface atoms of ZnO NWs leads to a drastic decrease in fracture 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".