Scholarlink

<p>Understanding how <i>processing</i>, <i>structure</i>, <i>properties</i>, and <i>performance</i> 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A multimodal dataset of causal mechanisms in materials science literature

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月16日

查看全文
  • Yinpeng Liu,
  • Congrui Wang,
  • Jiawei Liu,
  • Xiang Shi,
  • Yong Huang,
  • Qikai Cheng,
  • Wei Lu

摘要

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.