[Dataset] Lattice Metamaterials with Mesoscale Motifs: Exploration of Property Charts by Bayesian Optimisation
Bibliographic record
Abstract
[Dataset] Lattice Metamaterials with Mesoscale Motifs: Exploration of Property Charts by Bayesian Optimisation Roman Kulagin*, Patrick Reiser, Kyryl Truskovskyi, Arnd Koeppe, Yan Beygelzimer, Yuri Estrin, Pascal Friederich, Peter Gumbsch [*] Dr. R. Kulagin, Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany. E-Mail: roman.kulagin@kit.edu Dr. Patrick Reiser, Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany; Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Engler-Bunte-Ring 8, 76131 Karlsruhe, Germany. Kyryl Truskovskyi, Georgian, Toronto, Canada Dr. Arnd Koeppe, Institute for Applied Materials (IAM-MMS), Karlsruhe Institute of Technology, Straße am Forum 7, 76131 Karlsruhe, Germany. Prof. Pascal Friederich, Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany; Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Engler-Bunte-Ring 8, 76131 Karlsruhe, Germany. Prof. Y. Beygelzimer, Donetsk Institute for Physics and Engineering named after A.A. Galkin, National Academy of Sciences of Ukraine, Nauki ave., 46, 03028 Kyiv, Ukraine. Prof. Y. Estrin, Department of Materials Science and Engineering, Monash University, 22 Alliance Lane, Clayton 3800, Australia; Department of Mechanical Engineering, The University of Western Australia, Crawley 6009, Australia. Prof. P. Gumbsch, Institute for Applied Materials, Karlsruhe Institute of Technology, Straße am Forum 7, 76131, Karlsruhe, Germany; Fraunhofer Institute for Mechanics of Materials, Freiburg, Wöhlerstraße 11, 79108 Freiburg, Germany. Part of the work was supported by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) through the POLiS Cluster of Excellence (grant no. UP 33/1) under project ID 390874152 and by the Helmholtz association under the KNMFi program (grant no. 43.31.01).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".