Structuring scientific knowledge for smart materials and 4D printing: An ontology enrichment framework using retrieval-augmented multimodal large language models

4D printing – a rapidly evolving field that integrates additive manufacturing with stimuli-responsive materials – enables the creation of structures that change shape or properties over time. As the field advances, it poses significant challenges in synthesizing cross-disciplinary knowledge spanning manufacturing, materials science, chemistry, physics, engineering, and design. To address this, we propose a novel framework that leverages multimodal large language models and retrieval-augmented generation for ontology learning tailored to the 4D printing domain. This framework systematically extracts, maps, and interconnects insights from diverse sources, including scientific literature and experimental datasets, therefore massively enriching and populating a comprehensive multi-domain ontology. This enables the construction of interconnected knowledge graphs that capture relationships among material compositions, properties, stimuli interactions, process parameters, and design strategies. By bridging semantic gaps across involved disciplines, the framework enhances interpretability, supports informed material selection and distribution, and accelerates innovation in the development of programmable and multifunctional structures by 4D printing. Overall, this integrative approach represents a foundational step toward structuring a rapidly growing body of scientific efforts to enable advanced active materials and intelligent structures.