Inverse-designed growth-based cellular metamaterials

S. van 't Sant, P. Thakolkaran, Jonàs Martínez, Siddhant Kumar*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Advancements in machine learning have sparked significant interest in designing mechanical metamaterials, i.e., materials that derive their properties from their inherent microstructure rather than just their constituent material. We propose a data-driven exploration of the design space of growth-based cellular metamaterials based on star-shaped distances. These two-dimensional metamaterials are based on periodically-repeating unit cells consisting of material and void patterns with non-trivial geometries. Machine learning models exploiting large datasets are then employed to inverse design growth-based metamaterials for tailored anisotropic stiffness. Firstly, a forward model is created to bypass the growth and homogenization process and accurately predict the mechanical properties given a finite set of design parameters. Secondly, an inverse model is used to invert the structure–property maps and enable the accurate prediction of designs for a given anisotropic stiffness query. We successfully demonstrate the frameworks’ generalization capabilities by inverse designing for stiffness properties chosen from outside the domain of the design space.
Original languageEnglish
Article number104668
Number of pages11
JournalMechanics of Materials
Publication statusPublished - 2023


  • Cellular metamaterials
  • Machine learning
  • Inverse Design
  • Growth process


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