Abstract Machine learning models are increasingly used in many engineering fields thanks
to the widespread digital data, growing computing power, and advanced algorithms. The …

Mechanical metamaterials have been attracting increasing attention for a wide range of
industrial applications, such as energy storage, biomedical, sensors, robotics, etc., owing to …

This paper designs a novel reusable gradient negative stiffness (GNS) honeycomb structure
and focuses on investigating its energy absorption (EA). Contrary to classical normal …

Real-time tunable mechanical metamaterial is an emerging field attracting great attention. It
enables a wide range of mechanical properties but is hard to design and fabricate due to its …

In this work, we design and test a parameter optimization method by Python script to meet
the urgent demand for lightweight honeycomb metamaterial. The method mainly focuses on …

Artificial designed metamaterials have attracted widespread attention because of its unique
lattice structure and special physical properties. In this work, a chiral auxetic metamaterial …

Architected materials exhibit extraordinary properties in comparison with conventional
materials and structures, resulting in additional functionality and efficiency by engineering …

Negative stiffness (NS) mechanical metamaterials have excellent application prospect in a
broad range of fields such as energy trapping, repeated energy absorption and so on …

Compared with traditional energy dissipation structures, negative stiffness metamaterials
can repeatedly dissipate energy without incurring irreversible plastic deformation. The …

This study proposes a systematic inverse design framework for constructing multistable
mechanical metamaterials with programmable gradients. Herein, we designed the tailored …