Published June 20, 2024 | Version 1.0.0
Dataset Open

Computational Design of Multimodal Combinatorial Mechanical Metamaterials

  • 1. ROR icon University of Amsterdam
  • 2. ROR icon Institute for Atomic and Molecular Physics
  • 3. ROR icon Utrecht University
  • 4. ROR icon Leiden University

Description

This dataset contains the data used to design multimodal mechanical metamaterials as described in the paper 'Prospecting for Pluripotency in Metamaterial Design', as published in Phys. Rev. Research 7(2), 023299.

In this paper, the data is used to design 5×5 unit cells with desired deformation (zero) modes. The dataset contains the data used to train neural networks (CNN_data.zip), the designs generated by genetic algorithm (step_i.zip) and their mode structures (step_ii.zip), and the designs obtained through our design approach as described in the paper (step_ii.zip). Additionally, there is data comparing the efficiency of using a genetic algorithm or a hill climbing method to generate designs with a large number of intensive modes (step_i.zip).

Files

ID_CNN_data.zip

Files (5.4 GB)

Name Size Download all
md5:d8794d5d9867d69fdcaea4860f04de27
489.1 MB Preview Download
md5:0ed53c4c04e306f6ba6061837942f645
219.6 MB Preview Download
md5:73a83fc335ddf7ca9b5caec0466dc860
4.7 GB Preview Download

Additional details

Funding

European Commission
Extr3Me - Extreme Mechanics of Metamaterials: From ideal to realistic conditions 852587
European Commission
SoftML - Rational Design of Soft Hierarchical Materials with Responsive Functionalities: Machine learning Soft Matter to create Soft Machines 884902
Dutch Research Council
Achieving animate properties with odd robotic matter VI.Vidi.213.131

Software

Repository URL
https://uva-hva.gitlab.host/published-projects/inversecombimat
Programming language
Python
Development Status
Active