Deep learning-driven ultra-stretchable kirigami metamaterials: towards surface texture modulation via buckling

Yunce Zhang; Li Lin; Yafei Wang; Shurui Wang; Qiang Tao; Hongwei Liu; Qi Zhang; Changguo Wang

doi:10.1039/d5sm00933b

January 1, 2025Soft Matter

Deep learning-driven ultra-stretchable kirigami metamaterials: towards surface texture modulation via buckling

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Key Points

Key configuration enables surface texture modulation through innovative kirigami metamaterials, showing flexibility and responsiveness.
Performance analysis reveals strong adaptability under mechanical stress, improving material functionality across applications.
Assessment using forward-looking design strategies leads to enhanced mechanical performance predictions for various demands.
This approach supports the potential for advanced material applications but calls for further explorations in real-world scenarios.

Implication

This model demonstrates mechanical performance in metamaterials using deep learning, highlighting advanced texture modulation strategies.

Abstract

This paper presents a forward-looking kirigami design strategy that predicts required mechanical performance based on functional demands and enables functional configuration.

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Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/69255737c0ce034ddc35ad3b https://doi.org/10.1039/d5sm00933b

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Deep learning-driven ultra-stretchable kirigami metamaterials: towards surface texture modulation via buckling

Key Points

Implication

Abstract

Discussion

Cite This Study

Deep learning-driven ultra-stretchable kirigami metamaterials: towards surface texture modulation via buckling

Key Points

Implication

Abstract

Discussion

Cite This Study

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