Diatom-inspired architected materials using language-based deep learning: Perception, transformation and manufacturing

TL;DR

This paper introduces a novel approach using transformer neural networks and natural language models to generate biologically inspired diatom structures, which are then manufactured via additive manufacturing, demonstrating a new intersection of AI, biology, and manufacturing.

Contribution

It presents a new method leveraging language-based deep learning to design and produce diatom-inspired architected materials, bridging AI and bio-inspired manufacturing.

Findings

Successful generation of diatom-inspired designs using transformer models

Manufactured a diatom-based specimen with additive manufacturing

Demonstrated potential for expanding biologically inspired material design

Abstract

Learning from nature has been a quest of humanity for millennia. While this has taken the form of humans assessing natural designs such as bones, butterfly wings, or spider webs, we can now achieve generating designs using advanced computational algorithms. In this paper we report novel biologically inspired designs of diatom structures, enabled using transformer neural networks, using natural language models to learn, process and transfer insights across manifestations. We illustrate a series of novel diatom-based designs and also report a manufactured specimen, created using additive manufacturing. The method applied here could be expanded to focus on other biological design cues, implement a systematic optimization to meet certain design targets, and include a hybrid set of material design sets.