Cellular Automata Inspired Multistable Origami Metamaterials for Mechanical Learning (Adv. Sci. 34/2023)

Mechanical Learning In article number 2305146, Zuolin Liu, Hongbin Fang, Jian Xu, and Kon-Well Wang propose an innovative approach for mechanical learning through multistable origami metamaterials. Inspired by cellular automata, the multistable transition sequences are served as computing resources in the framework of reservoir computing. Digit recognition and complex computation tasks are successfully implemented through experiments by a single actuator, revolutionizing the potential for intelligent materials in diverse applications from computational science to mechano-intelligence.