Cephalo: Multi‐Modal Vision‐Language Models for Bio‐Inspired Materials Analysis and Design

Abstract Cephalo is presented as a series of multimodal vision large language models (V‐LLMs) designed for materials science applications, integrating visual and linguistic data for enhanced understanding. A key innovation of Cephalo is its advanced dataset generation method. Cephalo is trained on integrated image and text data from thousands of scientific papers and science‐focused Wikipedia data demonstrates it can interpret complex visual scenes, generate precise language descriptions, and answer queries about images effectively. The combination of a vision encoder with an autoregressive transformer supports multimodal natural language understanding, which can be coupled with other generative methods to create an image‐to‐text‐to‐3D pipeline. To develop more capable models from smaller ones, both mixture‐of‐expert methods and model merging are reported. The models are examined in diverse use cases that incorporate biological materials, fracture and engineering analysis, protein biophysics, and bio‐inspired design based on insect behavior. Generative applications include bio‐inspired designs, including pollen‐inspired architected materials, as well as the synthesis of bio‐inspired material microstructures from a photograph of a solar eclipse. Additional model fine‐tuning with a series of molecular dynamics results demonstrate Cephalo's enhanced capabilities to accurately predict statistical features of stress and atomic energy distributions, as well as crack dynamics and damage in materials.