Data underlying the publication: Inverse-designed growth-based cellular metamaterials

doi:10.4121/94939dc6-9f51-4f4a-a84b-ce660db0e7e0.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/94939dc6-9f51-4f4a-a84b-ce660db0e7e0
Datacite citation style:
Van 't Sant, Sikko; Thakolkaran, Prakash; Martínez, Jonàs; Kumar, Siddhant (2023): Data underlying the publication: Inverse-designed growth-based cellular metamaterials. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/94939dc6-9f51-4f4a-a84b-ce660db0e7e0.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

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.

history
  • 2023-05-03 first online, published, posted
publisher
4TU.ResearchData
format
PyTorch: *.pth
associated peer-reviewed publication
Inverse-designed growth-based cellular metamaterials
organizations
TU Delft, Faculty of Mechanical, Maritime and Materials Engineering (3mE), Department of Materials Science and Engineering
Université de Lorraine, CNRS, Inria, LORIA

DATA

files (3)