Prospecting for Pluripotency in Metamaterial Design
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915357818617856 |
|---|---|
| author | van Mastrigt, Ryan Dijkstra, Marjolein van Hecke, Martin Coulais, Corentin |
| author_facet | van Mastrigt, Ryan Dijkstra, Marjolein van Hecke, Martin Coulais, Corentin |
| contents | From self-assembly and protein folding to combinatorial metamaterials, a key challenge in material design is finding the right combination of interacting building blocks that yield targeted properties. Such structures are fiendishly difficult to find; not only are they rare, but often the design space is so rough that gradients are useless and direct optimization is hopeless. Here, we design ultra rare combinatorial metamaterials capable of multiple desired deformations by introducing a two-fold strategy that avoids the drawbacks of direct optimization. We first combine convolutional neural networks with genetic algorithms to prospect for metamaterial designs with a potential for high performance. In our case, these metamaterials have a high number of spatially extended modes; they are pluripotent. Second, we exploit this library of pluripotent designs to generate metamaterials with multiple target deformations, which we finally refine by strategically placing defects. Our pluripotent, multishape metamaterials would be impossible to design through trial-and-error or standard optimization. Instead, our data-driven approach is systematic and ideally suited to tackling the large and intractable combinatorial problems that are pervasive in material science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14980 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prospecting for Pluripotency in Metamaterial Design van Mastrigt, Ryan Dijkstra, Marjolein van Hecke, Martin Coulais, Corentin Soft Condensed Matter From self-assembly and protein folding to combinatorial metamaterials, a key challenge in material design is finding the right combination of interacting building blocks that yield targeted properties. Such structures are fiendishly difficult to find; not only are they rare, but often the design space is so rough that gradients are useless and direct optimization is hopeless. Here, we design ultra rare combinatorial metamaterials capable of multiple desired deformations by introducing a two-fold strategy that avoids the drawbacks of direct optimization. We first combine convolutional neural networks with genetic algorithms to prospect for metamaterial designs with a potential for high performance. In our case, these metamaterials have a high number of spatially extended modes; they are pluripotent. Second, we exploit this library of pluripotent designs to generate metamaterials with multiple target deformations, which we finally refine by strategically placing defects. Our pluripotent, multishape metamaterials would be impossible to design through trial-and-error or standard optimization. Instead, our data-driven approach is systematic and ideally suited to tackling the large and intractable combinatorial problems that are pervasive in material science. |
| title | Prospecting for Pluripotency in Metamaterial Design |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2406.14980 |