MatBind: A Shared Embedding Space for Multimodal Materials Characterization
Organizations: Institute for Advanced Simulations · Institute for Advanced Simulations (IAS-9), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany · Jülich Supercomputing Centre, Forschungszentrum Jülich · Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany · Institute of Nanotechnology, Karlsruhe Institute of Technology · Institute of Nanotechnology, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany · Helmholtz-Zentrum Berlin für Materialien und Energie · Helmholtz Institute for Polymers in Energy Applications Jena · Helmholtz-Zentrum Berlin für Materialien und Energie GmbH, Hahn-Meitner-Platz 1, 14109, Berlin, Germany · Helmholtz Institute for Polymers in Energy Applications Jena (Jena), Lessingstraße 12–14, 07743 Jena, Germany · 1. Physikalisches Institut, University of Cologne · 1. Phys Inst, University of Cologne, Zülpicher Str. 77, 50937, Köln, Germany · Laboratory of Organic and Macromolecular Chemistry, Friedrich Schiller University Jena · Center for Energy and Environmental Chemistry Jena, Friedrich Schiller University Jena · Laboratory of Organic and Macromolecular Chemistry, Friedrich Schiller University Jena, Humboldstr. 10, 07743 Jena, Germany · Center for Energy and Environmental Chemistry Jena, Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany · Faculty 5 - Georesources and Materials Engineering, RWTH Aachen University · Faculty 5 – Georesources and Materials Engineering, RWTH Aachen University, Aachen 52056, Germany
Abstract
Fully characterizing a crystalline material requires integrating heterogeneous data sources -- atomic structures, diffraction patterns, electronic density of states, and natural language -- each of which captures a different facet of the same physical object. In practice, however, these modalities are stored and analyzed in isolation, making it difficult to relate or query materials across representational boundaries. We present MatBind, a contrastive learning framework that aligns four materials modalities -- crystal structure, powder X-ray diffraction (pXRD) simulated from structures, density of states (DOS), and text -- into a unified embedding space using crystal structure as the central physical anchor. The framework induces alignment between modalities never explicitly paired during training, enabling emergent zero-shot cross-modal retrieval as a direct consequence of the shared representation. The learned embedding space organizes materials according to physically meaningful properties without explicit supervision, and retrieval performance improves systematically when modalities are combined at query time. These results demonstrate that treating heterogeneous materials data as complementary projections of a single physical reality, rather than as isolated data sources, is not a practical choice but is consistent with the underlying physics.