Designing materials such as zeolites is a major challenge due to the wide range of possible structures and synthesis conditions. Furthermore, knowledge on this subject is widely scattered across numerous publications and experimental notes. Therefore, large language models (LLMs) represent a promising tool for building knowledge bases, supporting the analysis of scientific literature, although their responses may be incomplete or fallible. In addition, in the case of zeolites, the relationship between material properties and their topology is of crucial importance, which limits the effectiveness of approaches based solely on text. In this study, we investigate a Retrieval-Augmented Generation (RAG) approach extended with a multimodal component that combines textual and structural information.