Journal
NPJ COMPUTATIONAL MATERIALS
Volume 7, Issue 1, Pages -Publisher
NATURE RESEARCH
DOI: 10.1038/s41524-021-00572-y
Keywords
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Funding
- National Science Foundation, Future Manufacturing Program [2036359]
- Aurora Early Science programs
- DOE Office of Science User Facility [DE-AC02-06CH11357]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [2036359] Funding Source: National Science Foundation
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The mechanical behavior of 2D materials like MoS2 can be manipulated through the ancient art of kirigami. By strategically inserting cuts, these materials can be stretched over 50%, but the design of kirigami structures with desired mechanical properties depends heavily on the pattern and location of cuts.
Mechanical behavior of 2D materials such as MoS2 can 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 MoS2 kirigami 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 MoS2 kirigami 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.
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