Scaling multimodal materials representation learning with synthetic narratives

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.