A versatile multimodal learning framework bridging multiscale knowledge for material design

Abstract

Artificial intelligence has achieved remarkable success in materials science, accelerating novel material design. However, real-world material systems exhibit multiscale complexity—spanning composition, processing, structure, and properties—posing significant challenges for modeling. While some approaches fuse multiscale features to improve prediction, important modalities such as microstructure are often missing due to high acquisition costs. Existing methods struggle with incomplete data and lack a framework to bridge multiscale material knowledge. To address this, we propose MatMCL, a structure-guided multimodal learning framework that jointly analyzes multiscale material information and enables robust property prediction with incomplete modalities. Using a self-constructed multimodal dataset of electrospun nanofibers, we demonstrate that MatMCL improves mechanical property prediction without structural information, generates microstructures from processing parameters, and enables cross-modal retrieval. We further extend it via multi-stage learning and apply it to nanofiber-reinforced composite design. MatMCL uncovers processing-structure-property relationships, suggesting its promise as a generalizable approach for AI-driven material design.