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

• URL: http://arxiv.org/abs/2405.19076v3
• Date: Mon, 15 Jul 2024 12:36:42 GMT
• Title: Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design
• Authors: Markus J. Buehler,
• Abstract summary: Cephalo is a series of vision large language models (V-LLMs) designed for materials science applications. It is trained on integrated image and text data from thousands of scientific papers. Generative applications include bio-inspired designs, including pollen-inspired architected materials.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present Cephalo, 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 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, we report both mixture-of-expert methods and model merging. We examine the models 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.

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