MatPROV: A Provenance Graph Dataset of Material Synthesis Extracted from Scientific Literature

Hirofumi Tsuruta, Masaya Kumagai

Keywords: material synthesis, synthesis procedure, provenance graph, PROV-DM, large language models, information extraction, knowledge extraction

TL;DR: We present MatPROV, a dataset of PROV-DM-compliant material synthesis procedures extracted from scientific literature using large language models.

Abstract: 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.

Submission Track: Paper Track (Short Paper)

Submission Category: Automated Synthesis

Institution Location: {Japan, Osaka}, {Japan, Kyoto}

Submission Number: 28