Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling

Li Zheng, Konstantinos Karapiperis, Siddhant Kumar*, Dennis M. Kochmann*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
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The rise of machine learning has fueled the discovery of new materials and, especially, metamaterials—truss lattices being their most prominent class. While their tailorable properties have been explored extensively, the design of truss-based metamaterials has remained highly limited and often heuristic, due to the vast, discrete design space and the lack of a comprehensive parameterization. We here present a graph-based deep learning generative framework, which combines a variational autoencoder and a property predictor, to construct a reduced, continuous latent representation covering an enormous range of trusses. This unified latent space allows for the fast generation of new designs through simple operations (e.g., traversing the latent space or interpolating between structures). We further demonstrate an optimization framework for the inverse design of trusses with customized mechanical properties in both the linear and nonlinear regimes, including designs exhibiting exceptionally stiff, auxetic, pentamode-like, and tailored nonlinear behaviors. This generative model can predict manufacturable (and counter-intuitive) designs with extreme target properties beyond the training domain.

Original languageEnglish
Article number7563
Number of pages14
JournalNature Communications
Issue number1
Publication statusPublished - 2023


Funding Information:
This research received financial support from Adidas as well as from ETH Zurich through the ETH+ grant SynMatLab. K.K. acknowledges the support from a Marie-Sklodowska Curie Postdoctoral Fellowship under Grant Agreement No. 101024077. The authors gratefully acknowledge the support from Adidas and the discussions with Dr. Ladan Salari-Sharif and Derek Luther.


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