(173a) Deep Composites: Bioinspired AI towards Modeling, Design and Manufacturing

Nature produces a variety of materials with many functions, often out of simple and abundant materials, and at low energy. Such systems - examples of which include silk, bone, nacre or diatoms - provide broad inspiration for engineering. Here we explore the translation of biological composites to engineering composites, using a variety of tools including molecular modeling, AI and machine learning, and experimental synthesis using 3D printing, and characterization. We review a series of studies focused on the mechanical behavior of materials, especially fracture, and how these phenomena can be modeled using a combination of molecular dynamics and machine learning. We present examples that involve deep convolutional neural networks, transformer neural networks, and game theoretical approaches towards design. One case study will cover a recent example that realizes a text-to-material design approach, developing new architected multimaterial composite designs based on human readable description and subsequent 3D printing – from word to matter. We conclude the talk with case studies of material optimization using genetic algorithms, applied to 3D printed composites, protein design, and a translation of protein folding to music and back to assess universal patterns.