Disorder unlocks the strength-toughness trade-off in metamaterials
Bibliographic record
Abstract
Disorder is ubiquitous in nature, found in both soft biological materials like leaves and strong, tough structures such as diatoms. However, its effect on mechanical properties – whether enhancing or degrading – remains poorly understood. To explore this, we generated 50,000 Voronoi network architectures with varying degrees of disorder and evaluated their mechanical response under uniaxial tensile stress using high-throughput finite-element simulations. Our analysis revealed two distinct failure mechanisms, with some disordered networks outperforming regular hexagonal honeycombs by up to 20% in strength and 100% in toughness, effectively overcoming the conventional strength-toughness trade-off. Remarkably, optimal architectures emerged across all disorder levels, challenging prior assumptions that such performance is achievable only with quasi-order. The mechanical impact of disorder is driven by local geometric features that determine whether the disorder has a positive or negative effect. By training Convolutional Neural Networks (CNNs) on this dataset, we accurately predicted mechanical properties, quickly identifying configurations that exceed traditional limits. This approach offers a pathway for designing lightweight, strong, and tough metamaterials by utilizing disorder to enhance mechanical performance.
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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 | 0.000 | 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".