Kirigami, the ancient paper art of cutting, has recently emerged as a new approach to construct metamaterials with novel properties imparted by cuts. However, most studies are limited to thin sheets‐based 2D kirigami metamaterials with specific forms and limited reconfigurability due to planar connection constraints of cut units. Here, 3D modular kirigami is introduced by cutting bulk materials into spatially closed‐loop connected cut cubes to construct a new class of 3D kirigami metamaterials. The module is transformable with multiple degrees of freedom that can transform into versatile distinct daughter building blocks. Their conformable assembly creates a wealth of reconfigurable and disassemblable metamaterials with diverse structures and unique properties, including reconfigurable 1D column‐like materials, 2D lattice‐like metamaterials with phase transition of chirality, as well as 3D frustration‐free multilayered metamaterials with 3D auxetic behaviors and programmable deformation modes. This study largely expands the design space of kirigami metamaterials from 2D to 3D.
Mechanical behavior of 2D materials such as MoS2can be tuned by the ancient art of kirigami. Experiments and atomistic simulations show that 2D materials can be stretched more than 50% by strategic insertion of cuts. However, designing kirigami structures with desired mechanical properties is highly sensitive to the pattern and location of kirigami cuts. We use reinforcement learning (RL) to generate a wide range of highly stretchable MoS2kirigami structures. The RL agent is trained by a small fraction (1.45%) of molecular dynamics simulation data, randomly sampled from a search space of over 4 million candidates for MoS2kirigami structures with 6 cuts. After training, the RL agent not only proposes 6-cut kirigami structures that have stretchability above 45%, but also gains mechanistic insight to propose highly stretchable (above 40%) kirigami structures consisting of 8 and 10 cuts from a search space of billion candidates as zero-shot predictions.
more » « less- NSF-PAR ID:
- 10273818
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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