Data underlying the publication: Inverse-designed growth-based cellular metamaterials
Our project aims to explore the design space of growth-based cellular metamaterials using a deep learning framework. These two-dimensional materials derive their properties from their microstructure rather than just their constituent material. We employ large datasets to develop forward and inverse models for designing metamaterials with tailored anisotropic stiffness. The forward model predicts mechanical properties based on design parameters, while the inverse model allows for the accurate prediction of designs based on anisotropic stiffness queries. Our framework's generalization capabilities are demonstrated by successfully designing for stiffness properties outside the design space domain. Here, we share the dataset we used to train our framework. More information on how to generate more data can be found in the README of this repository.
- 2023-05-03 first online, published, posted
Université de Lorraine, CNRS, Inria, LORIA