A multimodal dataset of causal mechanisms in materials science literature

Understanding how processing, structure, properties, and performance interact is essential for guiding materials design and discovery. Yet, causal mechanisms linking these elements are typically scattered across text, figures, and references in the literature, and efforts to systematically mine and organize such knowledge remain limited. In this work, we leverage an LLM-based mechanism deduction framework to construct a dataset of 207,200 fine-grained mechanisms with 1,113,940 multimodal evidences from 61,766 materials science research articles. Each mechanism is linked to a specific causal relation among the tetrahedral elements and is supported by evidence from experiment information, characterization results, and external knowledge, with its accuracy verified by materials science researchers. This dataset provides a large-scale, cross-validated collection of multimodal mechanism knowledge in materials science, serving as a resource for data-driven research and intelligent analysis.