Learning ordinality-aware multimodal representations for composite materials design
TL;DR
This work builds ORDER, a multimodal framework linking microstructures and descriptors with preserved property trends, aiding prediction, retrieval, and microstructure generation and consistently outperforms alignment-oriented and property-aware baselines across property prediction, cross-modal retrieval, and microstructure generation tasks.
Abstract
Composite materials design requires understanding complex microstructural characteristics, necessitating the integration of heterogeneous data sources with artificial intelligence. Current multimodal learning frameworks are mostly developed for crystalline or polymer systems with discrete structure-property mappings and well-defined structural graphs, but fail to model the continuous and nonlinear composite design spaces under data scarcity. Here we present ordinality as a core principle when building multimodal representations for composite materials. We propose ORDinal-aware imagE-tabulaR (ORDER) alignment to integrate microstructures with tabular material descriptors and apply physics-based surrogate signals to eliminate the need for full property annotation. ORDER ensures similar target properties occupy nearby regions in the latent space, preserving the continuous nature of composite properties and enabling meaningful interpolation between sparsely observed designs. Evaluated on nanofiber and carbon fiber composite datasets, ORDER consistently outperforms alignment-oriented and property-aware baselines across property prediction, cross-modal retrieval, and microstructure generation tasks. Composite materials design is difficult due to complex, continuous design space. This work builds ORDER, a multimodal framework linking microstructures and descriptors with preserved property trends, aiding prediction, retrieval, and microstructure generation.