Learning ORDER-Aware Multimodal Representations for Composite Materials Design

TL;DR

This paper introduces ORDER, a multimodal pretraining framework that captures the continuous and nonlinear design space of composite materials, enabling better property prediction and microstructure generation with limited data.

Contribution

The work presents a novel ordinal-aware multimodal learning approach tailored for composite materials, addressing the limitations of existing graph-based methods in continuous design spaces.

Findings

ORDER outperforms baseline models in property prediction tasks.

ORDER enables meaningful interpolation between sparse composite designs.

Physics-based ordinal signals improve pretraining without full property annotations.

Abstract

Artificial intelligence has shown remarkable success in materials discovery and property prediction, particularly for crystalline and polymer systems where material properties and structures are dominated by discrete graph representations. Such graph-central paradigm breaks down on composite materials, which possess continuous and nonlinear design spaces. General composite descriptors, e.g., fiber volume and misalignment angle, cannot fully capture the fiber distributions that determine microstructural characteristics, necessitating the integration of heterogeneous data sources through multimodal learning. Existing alignment-oriented frameworks have proven effective on abundant crystal or polymer data under discrete, unique graph-property mapping assumptions, but fail to address the highly continuous composite design space under extreme data scarcity. In this work we introduce ORDinal-aware imagE-tabulaR alignment (ORDER), a multimodal pretraining framework that establishes ordinality as a core principle for material representations. ORDER ensures that materials with similar target properties occupy nearby regions in the latent space, which effectively preserves the continuous nature of composite properties and enables meaningful interpolation between sparsely observed designs. We evaluate ORDER on a Nanofiber-reinforced composite dataset and a carbon fiber T700 dataset. ORDER and its variants outperform both alignment-oriented and customized property-aware contrastive baselines across property prediction, cross-modal retrieval, and microstructure generation tasks. We further introduce physics-based ordinal surrogate signals avoiding the need for full property annotation during pretrain. Our work demonstrates learning continuous multimodal features are fundamental for composite materials, and provides a reliable pathway toward data-efficient universal multimodal intelligent systems.