Robust reconfigurable modular metamaterials with demand-driven elasto-plastic properties

Haokai Zheng; Chunlei Li; Zitong Feng; Qiang Han; Xiaohu Yao

doi:10.1039/d6mh00688d

January 1, 2026Materials Horizons

Robust reconfigurable modular metamaterials with demand-driven elasto-plastic properties

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Key Points

This study aims to develop a blueprint for creating materials with adaptive mechanical properties using machine learning.
Utilized an advanced machine learning model to predict mechanical behaviors.
Explored a range of reconfigurable modular metamaterials with elasto-plastic characteristics.
Conducted experiments to validate real-time predictions of material properties.
Demonstrated effective reconfiguration of mechanical properties across various metamaterials.
Achieved accurate predictions of elasto-plastic behavior using machine learning methodologies.
Showed potential applications in structural protection and advanced material systems.

Implication

Demonstrates advanced material properties in modular systems, suggesting broad applications for structural protection.

Abstract

With robust reconfigurable mechanical properties and an accurate machine learning model, this study provides a blueprint for an intelligent system in advanced materials and structural protection, from modular carriers to real-time predictions.

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Cite This Study

Zheng et al. (2026) studied this question.

synapsesocial.com/papers/6a28fe9f6f82f25be989be3d https://doi.org/10.1039/d6mh00688d

View Full Paper

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Robust reconfigurable modular metamaterials with demand-driven elasto-plastic properties

Key Points

Implication

Abstract

Discussion

Cite This Study

Robust reconfigurable modular metamaterials with demand-driven elasto-plastic properties

Key Points

Implication

Abstract

Discussion

Cite This Study

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