Towards Engineering Material Neural Networks

Abstract

Structures that capture functionality in the form of animate or intelligent machines have the potential to transform modern engineering applications. Animation and embedded intelligence are typically realised by integrating advanced capabilities such as reversibility, adaptive responses and learning directly into the materials themselves. Currently, the majority of adaptive material systems rely on predefined adaptive designs combined with in-service, electronics-based computing to dynamically modify the structural behaviour. However, structural configurations with interconnected adaptable nodes are able to approximate continuous functions, providing new possibilities and opportunities than classical metamaterials and computational materials. We discuss here the potential to design load-bearing engineering materials with trainable physical parameters and neural network-inspired morphologies, embedding intelligence directly into their structure, a concept we define as Engineering Material Neural Networks (EMNNs) as a subcategory of Physical Neural Networks. In this perspective, we first establish the foundational concept of EMNNs; we then detail the mechanical and multifunctional properties required for such structural configurations. Finally, we evaluate existing and emerging engineering materials that hold promise for enabling this innovative approach. Key material candidates for realising EMNNs include composites, architected, biological and engineering living materials. We also outline future directions in materials science and structural engineering for developing EMNNs.