Digitally programmable kirigami metamaterials via magnetic encoding

The vast configuration space of magnetic metamaterials, enabled by embedding reversibly orientable magnets into rotating-square kirigami pixels, remains largely unexplored beyond uniform magnetization patterns. To navigate this space, in-plane magnetic orientations are treated as binary bits, creating 2N×N possible states for an N×N lattice. A Monte Carlo approach, combined with a mapping of all configurations onto an N2-dimensional hypercube, allows for the systematic statistical enumeration of energy landscapes and single-bit reconfiguration paths. This framework classifies stability into neutral, monostable, bistable, and tristable classes, with occurrence probabilities converging to approximately 0, 0.6013, 0.3981, and 0.0006 as system size increases. Programmable responses—including tension–compression asymmetric stiffness, snap-through instability, and a two-stage absorption/locking energy dissipation mode—are demonstrated. The resulting digital, graph-based platform points to applications in soft modular robotics, impact-mitigation layers, deployable structures, and mechanical logic.

Authors

Institutions

Publication Details

Journal
Applied Physics Letters
Published
2026-04-13
DOI
https://doi.org/10.1063/5.0318941
Primary Topic
Advanced Materials and Mechanics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Digitally programmable kirigami metamaterials via magnetic encoding

Jizhai Cui, Yongfeng Mei, Zhijun Lin, Jinbo yang et al.
Applied Physics Letters
Advanced Materials and Mechanics
article

Digitally programmable kirigami metamaterials via magnetic encoding

Jizhai Cui, Yongfeng Mei, Zhijun Lin, Jinbo yang, Qinlian Kang, Songyu Xiong
article en

Abstract

The vast configuration space of magnetic metamaterials, enabled by embedding reversibly orientable magnets into rotating-square kirigami pixels, remains largely unexplored beyond uniform magnetization patterns. To navigate this space, in-plane magnetic orientations are treated as binary bits, creating 2N×N possible states for an N×N lattice. A Monte Carlo approach, combined with a mapping of all configurations onto an N2-dimensional hypercube, allows for the systematic statistical enumeration of energy landscapes and single-bit reconfiguration paths. This framework classifies stability into neutral, monostable, bistable, and tristable classes, with occurrence probabilities converging to approximately 0, 0.6013, 0.3981, and 0.0006 as system size increases. Programmable responses—including tension–compression asymmetric stiffness, snap-through instability, and a two-stage absorption/locking energy dissipation mode—are demonstrated. The resulting digital, graph-based platform points to applications in soft modular robotics, impact-mitigation layers, deployable structures, and mechanical logic.

Applied Physics LettersVol. 128(15)
Nankai University (CN), Fudan University (CN), Yiwu Science and Technology Research Institute (CN)
National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality, Shanghai Rising-Star Program
Affordable and clean energy
Openalex Percentile: Top 23%
Advanced Materials and Mechanics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.