Work

Scaling multimodal materials representation learning with synthetic narratives

Year: 2026

Type: article

Abstract: Artificial intelligence is increasingly applied to inverse materials design, including crystals and molecules. For molecular systems, multimodal models that integrate chemical structures with natural language have enabled flexible, instruction-driven reasoning. However, approaches for crystalline materials remain limited by the scarcity and imbalanced bias of available crystal datasets and the weak semantic supervision provided by peer-reviewed literature. We introduce a contrastive language-crystals model (CLaC), a multimodal contrastive learning framework that aligns crystal structures with natural language, pre-trained on property-conditioned synthetic narratives of crystal structure-text pairs generated by language models. Here we show that, compared with models trained on literature-derived text, this property-grounded synthetic supervision enables semantic alignment unachievable with existing academic corpora. Embedding-space analyses reveal structured associations between language and crystal structures. These results establish a foundation for zero-shot, instruction-driven reasoning in crystalline materials. CLaC is a contrastive language-crystals model that aligns crystal structures with natural language using property-conditioned synthetic narratives, enabling zero-shot, instruction driven reasoning unachievable with literature-derived text alone.

Source: Nature Communications

DOI: 10.1038/s41467-026-76378-7

Authors: Yang Jeong Park, Mayank Kumaran, Chia-Wei Hsu, Elsa Olivetti, Ju Li

Institutions: Ulsan National Institute of Science and Technology, Massachusetts Institute of Technology, University of Illinois Urbana-Champaign

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $6,990