Learning ORDER-Aware Multimodal Representations for Composite Materials Design

• URL: http://arxiv.org/abs/2602.02513v1
• Date: Fri, 23 Jan 2026 06:39:01 GMT
• Title: Learning ORDER-Aware Multimodal Representations for Composite Materials Design
• Authors: Xinyao Li, Hangwei Qian, Jingjing Li, Ivor Tsang,
• Abstract summary: We introduce ORDinal-aware imagE-tabulaR alignment (ORDER), a multimodal pretraining framework that establishes ordinality as a core principle for composite material representations.<n>ORDER ensures that materials with similar target properties occupy nearby regions in the latent space, which effectively preserves the continuous nature of composite properties.
• Score: 15.819480337529741
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence (AI) 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 that lack well-defined graph structures. General composite descriptors, e.g., fiber volume and misalignment angle, cannot fully capture the fiber distributions that fundamentally determine microstructural characteristics, necessitating the integration of heterogeneous data sources through multimodal learning. Existing alignment-oriented multimodal 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 composite 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 public Nanofiber-enforced composite dataset and an internally curated dataset that simulates the construction of carbon fiber T700 with diverse fiber distributions. ORDER achieves consistent improvements over state-of-the-art multimodal baselines across property prediction, cross-modal retrieval, and microstructure generation tasks.

Related papers

• Deep learning-aided inverse design of porous metamaterials [0.0]
  The ultimate aim of the study is to explore the inverse design of porous metamaterials using a deep learning-based generative framework.<n>We develop a property-variational autoencoder (pVAE), a variational autoencoder (VAE) augmented with a regressor, to generate structured metamaterials with tailored hydraulic properties.
  arXiv  Detail & Related papers  (2025-07-23T20:07:53Z)
• Elucidating the Design Space of Multimodal Protein Language Models [69.3650883370033]
  Multimodal protein language models (PLMs) integrate sequence and token-based structural information.<n>This paper systematically elucidates the design space of multimodal PLMs to overcome their limitations.<n>Our advancements approach finer-grained supervision, demonstrating that token-based multimodal PLMs can achieve robust structural modeling.
  arXiv  Detail & Related papers  (2025-04-15T17:59:43Z)
• Causal Discovery from Data Assisted by Large Language Models [50.193740129296245]
  It is essential to integrate experimental data with prior domain knowledge for knowledge driven discovery.<n>Here we demonstrate this approach by combining high-resolution scanning transmission electron microscopy (STEM) data with insights derived from large language models (LLMs)<n>By fine-tuning ChatGPT on domain-specific literature, we construct adjacency matrices for Directed Acyclic Graphs (DAGs) that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO3 (SmBFO)
  arXiv  Detail & Related papers  (2025-03-18T02:14:49Z)
• MIND: Microstructure INverse Design with Generative Hybrid Neural Representation [25.55691106041371]
  inverse design of microstructures plays a pivotal role in optimizing metamaterials with specific, targeted physical properties.<n>We present a novel generative model that integrates latent diffusion with Holoplane, an advanced hybrid neural representation that simultaneously encodes both geometric and physical properties.<n>Our approach generalizes across multiple microstructure classes, enabling the generation of diverse, tileable microstructures with significantly improved property accuracy and enhanced control over geometric validity.
  arXiv  Detail & Related papers  (2025-02-01T20:25:47Z)
• Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks [52.13486402193811]
  New solid-state materials require rapidly exploring the vast space of crystal structures and locating stable regions. Existing methods struggle to explore large material spaces and generate diverse samples with desired properties and requirements. We propose a novel generative model employing a hierarchical exploration strategy to efficiently exploit the symmetry of the materials space to generate crystal structures given desired properties.
  arXiv  Detail & Related papers  (2024-11-06T23:53:34Z)
• Creating a Microstructure Latent Space with Rich Material Information for Multiphase Alloy Design [19.10106845551149]
  This study introduces an improved alloy design algorithm that integrates authentic microstructural information to establish precise CPSP relationships. The approach utilizes a deep-learning framework based on a variational autoencoder to map real microstructural data to a latent space.
  arXiv  Detail & Related papers  (2024-09-04T12:26:19Z)
• Defining Neural Network Architecture through Polytope Structures of Dataset [53.512432492636236]
  This paper defines upper and lower bounds for neural network widths, which are informed by the polytope structure of the dataset in question. We develop an algorithm to investigate a converse situation where the polytope structure of a dataset can be inferred from its corresponding trained neural networks. It is established that popular datasets such as MNIST, Fashion-MNIST, and CIFAR10 can be efficiently encapsulated using no more than two polytopes with a small number of faces.
  arXiv  Detail & Related papers  (2024-02-04T08:57:42Z)
• Compositional Representation of Polymorphic Crystalline Materials [56.80318252233511]
  We introduce PCRL, a novel approach that employs probabilistic modeling of composition to capture the diverse polymorphs from available structural information.<n>Extensive evaluations on sixteen datasets demonstrate the effectiveness of PCRL in learning compositional representation.
  arXiv  Detail & Related papers  (2023-11-17T20:34:28Z)
• Scalable Diffusion for Materials Generation [99.71001883652211]
  We develop a unified crystal representation that can represent any crystal structure (UniMat) UniMat can generate high fidelity crystal structures from larger and more complex chemical systems. We propose additional metrics for evaluating generative models of materials.
  arXiv  Detail & Related papers  (2023-10-18T15:49:39Z)
• Robust Model-Based Optimization for Challenging Fitness Landscapes [96.63655543085258]
  Protein design involves optimization on a fitness landscape. Leading methods are challenged by sparsity of high-fitness samples in the training set. We show that this problem of "separation" in the design space is a significant bottleneck in existing model-based optimization tools. We propose a new approach that uses a novel VAE as its search model to overcome the problem.
  arXiv  Detail & Related papers  (2023-05-23T03:47:32Z)
• DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure reconstruction from extremely small data sets [110.60233593474796]
  DA-VEGAN is a model with two central innovations. A $beta$-variational autoencoder is incorporated into a hybrid GAN architecture. A custom differentiable data augmentation scheme is developed specifically for this architecture.
  arXiv  Detail & Related papers  (2023-02-17T08:49:09Z)
• Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems [20.659457956055366]
  We propose a novel data-driven metamaterial design framework based on deep generative modeling. We show in this study that the latent space of VAE provides a distance metric to measure shape similarity. We demonstrate our framework by designing both functionally graded and heterogeneous metamaterial systems.
  arXiv  Detail & Related papers  (2020-06-27T03:56:55Z)
• Intelligent multiscale simulation based on process-guided composite database [0.0]
  We present an integrated data-driven modeling framework based on process modeling, material homogenization, and machine learning. We are interested in the injection-molded short fiber reinforced composites, which have been identified as key material systems in automotive, aerospace, and electronics industries.
  arXiv  Detail & Related papers  (2020-03-20T20:39:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.