用大模型从文献中提取材料合成过程，构建可机器理解的图谱数据集。
MatPROV: A Provenance Graph Dataset of Material Synthesis Extracted from Scientific Literature
- 基于PROV-DM标准，用图结构建模合成流程，支持灵活表达复杂关系。
- 构建了首个符合PROV-DM规范的材料合成溯源数据集MatPROV。
- 适合做材料自动化合成规划与优化的研究者使用。
合成程序在材料研究中至关重要，直接影响材料性能。随着数据驱动方法加速材料发现，从科学文献中提取合成程序并转化为结构化数据成为热点。然而，现有研究多依赖固定领域模板或假设合成过程为线性序列，难以捕捉真实流程的结构复杂性。为此，本文采用国际标准PROV-DM，支持灵活的图结构建模。我们提出MatPROV，一个基于大语言模型从文献中提取的、符合PROV-DM规范的合成程序数据集。该数据集通过直观的有向图表示材料、操作与条件间的因果关系和复杂结构，实现可机器理解的合成知识表达，为未来自动化合成规划与优化研究提供基础。
原文摘要 · Abstract (English)
Synthesis procedures play a critical role in materials research, as they directly affect material properties. With data-driven approaches increasingly accelerating materials discovery, there is growing interest in extracting synthesis procedures from scientific literature as structured data. However, existing studies often rely on rigid, domain-specific schemas with predefined fields for structuring synthesis procedures or assume that synthesis procedures are linear sequences of operations, which limits their ability to capture the structural complexity of real-world procedures. To address these limitations, we adopt PROV-DM, an international standard for provenance information, which supports flexible, graph-based modeling of procedures. We present MatPROV, a dataset of PROV-DM-compliant synthesis procedures extracted from scientific literature using large language models. MatPROV captures structural complexities and causal relationships among materials, operations, and conditions through visually intuitive directed graphs. This representation enables machine-interpretable synthesis knowledge, opening opportunities for future research such as automated synthesis planning and optimization.
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