Words to Matter: De novo Architected Materials Design Using Transformer Neural Networks

Abstract

<jats:p>Transformer neural networks have become widely used in a variety of AI applications, enabling significant advances in Natural Language Processing (NLP) and computer vision. Here we demonstrate the use of transformer neural networks in the <jats:italic>de novo</jats:italic> design of architected materials using a unique approach based on text input that enables the design to be directed by descriptive text, such as “<jats:italic>a regular lattice of steel</jats:italic>”. Since transformer neural nets enable the conversion of data from distinct forms into one another, including text into images, such methods have the potential to be used as a natural-language-driven tool to develop complex materials designs. In this study we use the Contrastive Language-Image Pre-Training (CLIP) and VQGAN neural networks in an iterative process to generate images that reflect text prompt driven materials designs. We then use the resulting images to generate three-dimensional models that can be realized using additive manufacturing, resulting in physical samples of these text-based materials. We present several such word-to-matter examples, and analyze 3D printed material specimen through associated additional finite element analysis, especially focused on mechanical properties including mechanism design. As an emerging new field, such language-based design approaches can have profound impact, including the use of transformer neural nets to generate machine code for 3D printing, optimization of processing conditions, and other end-to-end design environments that intersect directly with human language.</jats:p>