Model elasto-plastic metamaterials as generic computing platforms

Abstract

Mechanical metamaterials are engineered materials constructed from elementary building blocks, typically arranged in regular patterns. These materials are generally studied for their effective properties, which are determined by the arrangement of their building blocks rather than the material they are made of. However, new functionalities are emerging, with mechanical metamaterials now exhibiting capabilities similar to conventional computers. For instance, they have been used to store binary data and even perform simple computations such as small binary additions and multiplications. These computing devices could be valuable in extreme environments where traditional electronic systems fail to operate. Moreover, they can process data autonomously without requiring a sustained power source. Despite this potential, there are no established design principles for systematically developing such computational materials. In this work, we explore the use of a model elasto-plastic metamaterial – a two-dimensional lattice composed of linear and bistable spring–mass systems – for executing sequential algorithms. While previous studies on mechanical computing mostly focused on small devices, we show that the lattice can be crafted to execute algorithms with many steps and large inputs, such as $n$-bit binary number additions. To our knowledge, this model has never been used for computational purposes before. This work thus offers a novel perspective on such models, proposing them as generic computing platforms that can be harnessed to design new computational materials.