Computational Design of Multimodal Combinatorial Mechanical Metamaterials
Authors/Creators
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
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