Lattice Metamaterials with Mesoscale Motifs: Exploration of Property Charts by Bayesian Optimization
Bibliographic record
Abstract
Through the current work, the usefulness of the concept of architectured rod lattices based on unit cell motifs designed at mesoscale is demonstrated. Specifically, 2D triangular lattices with unit cells containing different numbers of rods are considered. Combinations of rods of two different types provide the lattices explored with a greater complexity and versatility. For mesocells with a large number of variable parameters, it is virtually impossible to calculate the entire set of the points mapping the material onto its property space, as the volume of calculations would be gigantic. The number of possible motifs increases exponentially with the number of rods. Herein, the lattice metamaterials with mesoscale motifs are investigated with the focus on their elastic properties by combining machine learning techniques (specifically, Bayesian optimization) with finite element computations. The proposed approach made it possible to construct property charts illustrating the evolution of the boundary of the elastic compliance tensor of lattice metamaterials with an increase in the number of rods of the mesocell when a full‐factor experiment would not be possible.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".