Data underlying the publication: Inverse-designed growth-based cellular metamaterials
DOI: 10.4121/94939dc6-9f51-4f4a-a84b-ce660db0e7e0
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.ResearchDataFormat
PyTorch: *.pthAssociated peer-reviewed publication
Inverse-designed growth-based cellular metamaterialsOrganizations
TU Delft, Faculty of Mechanical, Maritime and Materials Engineering (3mE), Department of Materials Science and EngineeringUniversité de Lorraine, CNRS, Inria, LORIA
DATA
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- 3,091 bytesMD5:
28ac42037fc1626e64cf42dbc2723909
README.md - 160,976,751 bytesMD5:
1c0583725b80ba888dde0470f1fd7dcb
X_data.pth - 72,852,655 bytesMD5:
cde4fbf25444c0842d7d5b76fd20adde
y_data.pth -
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