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.
Machine learning methods have recently shown promise in predicting composite material properties more efficiently than traditional finite element simulations. However, in real-world applications of composite materials, variations in filler characteristics or processing conditions can cause a significant mismatch between the training and test distributions, leading to degradation in model performance. Existing approaches to domain adaptation typically aim to learn feature representations that are invariant across distribution shifts or minimize worst case risk across predefined environments, which are not suitable for composite materials where the distribution shifts are feature driven and localized. In this work, we argue that such shifts are more naturally characterized by the geometry of the data manifold rather than Euclidean distances in the ambient feature space. We reinterpret OOD generalization under covariate shift as a problem of preserving geodesic relationships on the data manifold. Building on this perspective, we propose Geometry Support Anchoring (GSA), a geometry-aware learning framework that anchors predictions using geodesic distances to data-dependent reference representations. For our approach, we have developed a multimodal conditional Joint Embedding Predictive Architecture (JEPA) that learns invariant, material-relevant representations by predicting latent targets using context from complementary modalities. By enforcing consistency in intrinsic geometry rather than raw features, our approach preserves physically meaningful variation while improving robustness to regime shifts. Experiments on both public and in-house composite material datasets with realistic distribution shifts demonstrate that our method consistently improves robustness and OOD generalization compared to state-of-the-art tabular learning and domain adaptation methods.
Abhiroop Bhattacharya, Hangwei Qian, Sylvain G. Cloutier et al.· Proceedings of the 32nd ACM...· 0 citations
MatBind is presented, a contrastive learning framework that aligns four materials modalities -- crystal structure, powder X-ray diffraction simulated from structures, density of states, and text -- into a unified embedding space using crystal structure as the central physical anchor and induces alignment between modalities never explicitly paired during training.
Le Yang, A. Chandran, Jona Ostreicher et al.· 0 citations
A materials property hierarchy is introduced, from intrinsic, composition-determined properties to extrinsic, processing-dependent performance, to clarify deployment constraints and distinguish structural, physical and deployment novelty.
Xianyuan Liu, Charles Anjah, Benjamin E. Jolly et al.· Journal of Physics Materials· 0 citations
Diffusion-based generative models with property guidance have emerged as a promising paradigm for inverse materials design by enabling the generation of crystalline materials with user-specified target properties. However, despite recent advances, the effectiveness of property guidance, its influence on crystallographic symmetry, and the physical viability of generated materials remain poorly understood. To address these questions, we develop a property-guided framework based on the lightweight diffusion model DiffCrysGen using parameter-efficient adapter fine-tuning and classifier-free guidance (CFG). The resulting framework enables efficient multi-property crystal generation while preserving the knowledge learned during unconditional pre-training. Using formation energy together with saturation magnetization and Vickers hardness as representative inverse-design tasks, we systematically investigate the influence of CFG across a broad range of guidance strengths. Increasing the guidance scale progressively steers the generated property distributions toward the prescribed targets while reducing the fraction of lowest-symmetry ($P1$) structures and increasing the proportion of higher-symmetry structures. To evaluate physical viability, generated structures are geometrically prescreened and subsequently validated using a machine-learning interatomic potential (MLIP)-based workflow comprising structural relaxation and thermodynamic, dynamical, and property-specific analyses. The framework identifies thermodynamically and dynamically stable magnetic and mechanically hard materials with overall success rates of 12.3\% and 3.9\%, respectively. These results establish property-guided DiffCrysGen as an efficient framework for inverse materials design while providing new insights into the role of classifier-free guidance in crystal generation.
Small-data inverse design is challenging in engineering informatics when observations are heterogeneous, mixed-type, and constrained by physical relations among design variables. This work proposes a topology-aware surrogate framework guided by an Incremental Transformer (INCRT) for physics-constrained inverse design, applied to geopolymer mixture design. The method integrates intrinsic-dimensionality analysis, mixed-variable design-space representation, tabular surrogate prediction, INCRT-based manifold rationalisation, and constrained inverse optimisation. Using a public benchmark of fly-ash and slag-based geopolymer concrete mixtures with compressive-strength and carbon-emission targets, the high-dimensional design space proves strongly redundant, organising around fewer effective mixture regimes. Compressive strength requires nonlinear tabular surrogates, while carbon emission is largely determined by composition and well recovered by regularised linear models. INCRT thus acts not as a replacement for tabular predictors but as a rationalisation layer providing prototype regimes and a manifold-support score for inverse design. Three strategies are compared: unconstrained surrogate optimisation, physics-constrained optimisation, and topology-aware physics-constrained optimisation. Unconstrained optimisation can match target strength but may yield physically invalid or off-manifold candidates; physics-only constraints do not always ensure data support. The topology-aware strategy yields candidates balancing target compliance, carbon reduction, physical admissibility, and proximity to the learned feasible manifold. The framework aims not to replace experimental validation but to support screening of credible candidate mixtures from small, mixed, physically constrained engineering datasets.