Disordered kirigami metamaterials achieve programmable anisotropy

Researchers have developed a novel approach to kirigami metamaterials by introducing controlled disorder, moving beyond traditional periodic cut patterns. This engineered disorder allows for programmable anisotropy and significantly reduces the coupling between extension and shear, enabling materials that can stretch along one axis without parasitic shear. A geometry-aware graph neural network, trained with a genetic algorithm, was used to navigate the complex design space and predict the mechanical responses, which were then successfully fabricated and validated in elastomer samples. AI

IMPACT Introduces a new method for designing materials with tunable mechanical properties using AI, potentially impacting fields requiring precise material deformation.

RANK_REASON Academic paper detailing a new method for designing materials. [lever_c_demoted from research: ic=1 ai=0.7]

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• elastomer
• genetic algorithm
• graph neural network
• kirigami

• paper
• other

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